CN113758503B - Process parameter estimation method and device, electronic equipment and storage medium - Google Patents
Process parameter estimation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113758503B CN113758503B CN202110924502.3A CN202110924502A CN113758503B CN 113758503 B CN113758503 B CN 113758503B CN 202110924502 A CN202110924502 A CN 202110924502A CN 113758503 B CN113758503 B CN 113758503B
- Authority
- CN
- China
- Prior art keywords
- variable
- parameter
- determining
- magnitude
- noise signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D3/00—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups
- G01D3/02—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups with provision for altering or correcting the law of variation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
Landscapes
- Engineering & Computer Science (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The embodiment of the application provides a process parameter estimation method, a process parameter estimation device, electronic equipment and a storage medium. The method comprises the steps of collecting a measured value of a first variable according to a preset sampling interval time; acquiring a first parameter and a second parameter of the first variable according to the measured value, wherein the first parameter is used for representing the influence degree of the noise signal on the increment of the first variable in a preset sampling interval time, and the second parameter is used for representing the change degree of the measured value along with the time; determining the magnitude of the noise signal according to the first parameter and the second parameter; determining a posterior estimate of the second variable based on the magnitude of the noise signal; wherein the first variable has a functional relationship with the second variable. Therefore, the scheme of the application can improve the estimation precision of the process parameters, thereby shortening the response time for reaching the same precision.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for estimating process parameters, an electronic device, and a storage medium.
Background
In the state monitoring system, the process parameters reflect the working state of the whole system, and are the basis and key for the normal working of the system. Therefore, the rapid and accurate estimation of the state monitoring process parameters is of great significance to safe production.
In the existing state monitoring process parameter estimation technology, the estimation precision and the response time have higher requirements on accurate measurement of initial stage signals. In some condition monitoring systems, however, the initial phase signal amplitude is small and is highly affected by noise, thus greatly interfering with process parameter estimation. Namely, the process parameters are estimated by adopting a calculation method depending on the signals at the initial stage, the problem of poor stability of the calculation result occurs when the signal amplitude at the initial stage is small, and the problems of long measurement response time and poor real-time performance occur when the signal amplitude at the initial stage is large.
Therefore, the process parameter estimation method in the prior art is greatly influenced by system noise interference, so that the estimation result precision is poor, and the response time required for achieving high precision is long.
Disclosure of Invention
The embodiment of the application provides a process parameter estimation method, a process parameter estimation device, electronic equipment and a storage medium, so that the estimation precision of process parameters is improved, and the response time for achieving the same precision is shortened.
In order to solve the technical problem, the present application is implemented as follows:
in a first aspect, an embodiment of the present application provides a method for estimating a process parameter, where the method includes:
collecting a measured value of a first variable according to a preset sampling interval time;
acquiring a first parameter and a second parameter of the first variable according to the measured value, wherein the first parameter is used for representing the influence degree of a noise signal on the increment of the first variable in the preset sampling interval time, and the second parameter is used for representing the change degree of the measured value along with the time;
determining the magnitude of the noise signal according to the first parameter and the second parameter;
determining a posteriori estimate of a second variable based on the magnitude of the noise signal;
wherein the first variable has a functional relationship with the second variable.
In a second aspect, an embodiment of the present application provides a process parameter estimation apparatus, including:
the acquisition module is used for acquiring the measured value of the first variable according to the preset sampling interval time;
a parameter obtaining module, configured to obtain a first parameter and a second parameter of the first variable according to the measured value, where the first parameter is used to indicate a degree of influence of a noise signal on an increment of the first variable in the preset sampling interval time, and the second parameter is used to indicate a degree of change of the measured value with time;
a noise magnitude determination module, configured to determine a magnitude of the noise signal according to the first parameter and the second parameter;
a first estimation module for determining a posteriori estimate of a second variable based on the magnitude of the noise signal;
wherein the first variable has a functional relationship with the second variable.
In a third aspect, an embodiment of the present application additionally provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the process parameter estimation method according to the first aspect.
In a fourth aspect, embodiments of the present application additionally provide a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the process parameter estimation method according to the first aspect.
In the embodiment of the application, the measured value of the first variable is acquired according to a preset sampling interval, so that a first parameter and a second parameter of the first variable are acquired according to the measured value, wherein the first parameter is used for representing the influence degree of a noise signal on the increment of the first variable in the preset sampling interval time, and the second parameter is used for representing the linear change degree of the measured value along with the time; and then determining the magnitude of the noise signal according to the first parameter and the second parameter, and determining the posterior estimation value of the second variable having a functional relation with the first variable based on the magnitude of the noise signal.
Therefore, according to the embodiment of the application, the magnitude of the noise signal is estimated according to the measured value of the first variable, so that the posterior estimation of the second variable which has a functional relation with the first variable is determined according to the magnitude of the noise signal. Therefore, even if the system is unstable in the initial stage, the magnitude of the system noise can be accurately estimated, and further, the process parameters can be more accurately estimated (namely, the posterior estimation value of the second variable is more accurately determined) based on the estimated noise magnitude. Therefore, the embodiment of the application can relieve the influence of system noise on the process parameter estimation, thereby achieving higher estimation precision and shortening the response time for achieving the same precision.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart illustrating steps of a method for estimating process parameters according to an embodiment of the present application;
FIG. 2 shows an embodiment of the present application
Measured value of and
a comparison of the measured values of (a);
FIG. 3 is a plot of the mean deviation ratio Para and the correlation calculated in the example of the present applicationNumber of
A schematic diagram of (a);
FIG. 4 is a comparison of the effect of using different algorithms to determine the posterior estimates of T in the examples of the present application;
fig. 5 is a block diagram of a process parameter estimation apparatus according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The load identification method of the edge calculation in the embodiment of the application can be operated on a terminal device or a server. The terminal device may be a local terminal device. When the method operates as a server, it can be presented as a cloud.
In an optional embodiment, the cloud presentation refers to an information presentation manner based on cloud computing. In the cloud display operation mode, an operation main body and an information picture presentation main body of an information processing program are separated, storage and operation of a display switching method are completed on a cloud display server, and a cloud display client is used for receiving and sending data and presenting an information picture, for example, the cloud display client can be a display device with a data transmission function close to a user side, such as a mobile terminal, a television, a computer, a palm computer and the like; however, the terminal device for processing the information data is a cloud display server at the cloud end. When browsing, a user operates the cloud display client to send an operation instruction to the cloud display server, the cloud display server performs coding compression on data according to operation instruction display information, returns the data to the cloud display client through a network, and finally decodes the data through the cloud display client and outputs display content.
In another alternative embodiment, the terminal device may be a local terminal device. The local terminal device stores an application program and is used for presenting an application interface. The local terminal device is used for interacting with a user through a graphical user interface, namely, downloading and installing an application program through the electronic device and running the application program conventionally. The manner in which the local terminal device provides the graphical user interface to the user may include a variety of ways, for example, it may be rendered for display on a display screen of the terminal or provided to the user by holographic projection. For example, the local terminal device may include a display screen for presenting a graphical user interface including an application screen and a processor for running the application, generating the graphical user interface, and controlling display of the graphical user interface on the display screen.
The process parameter estimation method provided by the embodiment of the present application is explained in detail below.
Referring to fig. 1, there is shown a flow chart of the steps of a process parameter estimation method in an embodiment of the present application, the method comprising the following steps 101 to 104.
Step 101: and acquiring the measured value of the first variable according to the preset sampling interval time.
Wherein the measured value of the first variable is a value containing the system noise (i.e. noise signal) of the system to be monitored. Thus, embodiments of the present application estimate a posteriori estimates of a second variable that is a function of a first variable based on a measurement of the first variable that includes system noise.
Step 102: and acquiring a first parameter and a second parameter of the first variable according to the measured value.
Wherein the first parameter is used for representing the influence degree of a noise signal on the increment of the first variable in the preset sampling interval time, and the second parameter is used for representing the change degree of the measured value along with the time.
Step 103: determining the magnitude of the noise signal according to the first parameter and the second parameter.
In an embodiment of the present application, by calculating the first parameter and the second parameter of the first variable, the influence of the noise signal can be quantified, thereby providing a basis for subsequently determining the posterior estimate of the second variable.
Step 104: an a posteriori estimate of a second variable is determined based on the magnitude of the noise signal.
Wherein the first variable has a functional relationship with the second variable.
As can be seen from the foregoing steps 101 to 104, in the embodiment of the present application, the measured value of the first variable may be acquired according to the preset sampling interval, so as to obtain the first parameter and the second parameter of the first variable according to the measured value, where the first parameter is used to indicate a degree of influence of the noise signal on an increment of the first variable in the preset sampling interval, and the second parameter is used to indicate a degree of linear change of the measured value with time; and then determining the magnitude of the noise signal according to the first parameter and the second parameter, and determining the posterior estimation value of the second variable having a functional relation with the first variable based on the magnitude of the noise signal.
Therefore, according to the embodiment of the application, the magnitude of the noise signal is estimated according to the measured value of the first variable, so that the posterior estimation of the second variable which has a functional relation with the first variable is determined according to the magnitude of the noise signal. Therefore, even if the system is unstable in the initial stage, the magnitude of the system noise can be accurately estimated, and further, the process parameters can be more accurately estimated (namely, the posterior estimation value of the second variable is more accurately determined) based on the estimated noise magnitude. Therefore, the embodiment of the application can relieve the influence of system noise on the process parameter estimation, thereby achieving higher estimation precision and shortening the response time for achieving the same precision.
Optionally, before the collecting the measured value of the first variable according to the preset sampling interval time, the method further includes:
acquiring the monitoring quantity and the quantity to be calculated of a system to be monitored;
acquiring a linear function relation expression between the monitored quantity and the quantity to be solved, wherein a dependent variable of the linear function relation expression comprises the monitored quantity, and a coefficient of an independent variable of the linear function relation expression comprises the quantity to be solved;
and determining the dependent variable of the linear function relational expression as the first variable, and determining the coefficient of the independent variable of the linear function relational expression as the second variable.
In a system to be monitored (e.g., a condition monitoring system), accurate acquisition of a source signal (i.e., acquisition of a measurement value of a monitored quantity) is a first step of process parameter estimation and is a crucial loop. Generally, the monitored quantity will have a linear trend over time, or a linear variation in deformation, such as an exponential variation. And the expression of the monitoring quantity changing along with the time comprises the quantity to be required. In order to simplify the process of determining the posterior estimate of the quantity to be evaluated from the measured value of the monitored quantity (i.e., to simplify the process of estimating the process parameters), the embodiment of the present application needs to convert the functional relationship between the monitored quantity and the quantity to be evaluated into a linear form, and in the functional relationship of the linear form, the monitored quantity is used as a part of the independent variable and the quantity to be evaluated is used as a part of the coefficient of the dependent variable.
Therefore, in the embodiment of the present application, a functional relationship exists between the monitored quantity and the quantity to be obtained of the system to be monitored, and the functional relationship may be a linear functional relationship or a nonlinear functional relationship. The functional relationship between the monitored quantity and the quantity to be calculated of the system to be monitored can be converted into a form of 'the monitored quantity is used as a part of the dependent variable, and the quantity to be calculated is used as a part of the coefficient of the dependent variable' no matter the functional relationship between the monitored quantity and the quantity to be calculated of the system to be monitored belongs to a linear functional relationship or a nonlinear functional relationship.
In this way, the converted linear functional relational expression includes a dependent variable of the monitored quantity as the first variable, and includes a coefficient of the quantity to be obtained as the second variable. That is, in this case, the posterior estimated value of the second variable can be determined from the measured value of the first variable, and the posterior estimated value of the amount to be determined can be obtained from the posterior estimated value of the second variable.
For example:
wherein, V is the monitoring quantity, M is the quantity to be solved, t represents time, L is the known quantity, then V is the first variable, and 2M is the second variable.
Alternatively, for example:
wherein, in the step (A),
t is the amount to be measured, T represents the time,
、
、
is a known amount, then
Can be converted into:
wherein, in the step (A),
is equivalent noise, then
In order to be the first variable mentioned above,
the second variable is as described above.
Optionally, after determining the posterior estimate of the second variable based on the magnitude of the noise signal, the method further includes:
and determining the posterior estimated value of the quantity to be solved according to the posterior estimated value of the second variable.
The second variable is a coefficient of an independent variable in a linear expression of functional relation conversion between the monitoring quantity and the quantity to be solved, and the coefficient of the independent variable comprises the quantity to be solved, so that after the posterior estimation value of the second variable is obtained, the posterior estimation value of the quantity to be solved can be further obtained according to the posterior estimation value of the second variable. For example, in the foregoing example, the second variable is
If the waiting quantity is T, then the determination is made
After the posterior estimate of (a), a posterior estimate of T can be obtained.
Optionally, the obtaining a first parameter and a second parameter of the first variable according to the measured value includes:
moving a preset sliding window according to a preset step length from the sampling start time of the first variable, and calculating the first parameter and the second parameter of the first variable according to the measured value in the sliding window after moving the sliding window once;
the preset step length comprises a preset number of sampling points.
Therefore, in the embodiment of the application, the preset sliding window is moved according to the preset step length, and the first parameter and the second parameter of the first variable are calculated once when the sliding window is moved once, so that the magnitude of the noise signal of the current system to be monitored is determined according to the calculated first parameter and second parameter. In other words, in the embodiment of the present application, noise estimation is performed once every time the sliding window is moved, so that dynamic estimation of a noise signal of a system to be monitored is achieved, and a posterior estimation value of the second variable can be dynamically obtained according to the noise magnitude. In this way, the accuracy of the a posteriori estimates of the second variable is further improved.
Optionally, the process of calculating the first parameter of the first variable according to the measured value in the sliding window includes:
determining a discrete sequence of differential signals, wherein the discrete sequence comprises the measurements within the sliding window;
calculating the mean and standard deviation of the differential signal;
and calculating the ratio of the standard deviation to the average value to obtain the first parameter.
Wherein the signal difference may further reveal the influence of noise signals during the measurement process on the measurement signal, i.e. the first variable. For a linear signal, its mathematical basic form is a linear function. Therefore, the theoretical mathematical form of the differential signal is a constant, reflecting the rate of increase of the measurement signal over time.
Assuming that the discrete sequence of measured values of the first variable within the sliding window is x (k), the differential signal is expressed as follows:
if the sampling interval time is Δ t, the differentiated signal is an increment of the linear signal (i.e., the first variable) within the time of Δ t. In general, in an initial measurement stage, due to the fact that the influence of noise is large, the fluctuation of a differential signal is also large, and further, the calculation of signal parameters is greatly influenced.
In addition, after the discrete sequence is subjected to differential processing, the obtained differential signal reflects the influence degree of the noise signal on the first variable. The influence of the noise signal is weakened gradually along with the measurement time, and the influence degree of the noise signal on the first variable can be adaptively quantized by calculating the first parameter and the second parameter of the first variable, so that a basis is provided for subsequently determining the posterior estimation value of the second variable which has a functional relation with the first variable.
Furthermore, the calculation process of the first parameter of the first variable is as follows:
for example, if the sliding window includes n sampling points, the measured values in the sliding window are discrete
The differential signal of sequence x (k) is: y (k), wherein k is an integer of 0 to n-1. The average value of the differential signal is
Standard deviation of
。
Wherein the average value
Reflecting the mean level over the values of the differential signal, while the standard deviation
The amplitude of the fluctuation of the differential signal around the mean value is reflected, and the combination of the two reflects the degree of influence of the noise signal on the increment of the first variable in the preset sampling interval time. Therefore, the ratio of the average value to the standard deviation of the differential signal can be used as the first parameter. The ratio of the mean to the standard deviation of the difference signal may also be referred to as the mean of deviation ratio, and is denoted as Para.
In summary, the first parameter is expressed as a deviation-to-mean ratio,
。
optionally, the process of calculating the second parameter of the first variable according to the measured value in the sliding window includes:
calculating a correlation coefficient of a discrete sequence comprising the measurement values within the sliding window with a sampling time of the measurement values and determining the correlation coefficient as the second parameter.
Where the correlation coefficient is a measure of the degree of linear correlation between the study variables. For the discrete sequence x (k) and the discrete measurement time point k, the correlation coefficients of the two are reversedThe degree of linear variation of the dispersion with time is mapped, with the larger the absolute value of the correlation coefficient (i.e., approaching 1), the more linearly the discrete sequence is related to time. If the random variable X represents a discrete sequence and the random variable K represents a discrete time sequence, the expression of the correlation coefficient between the two is:
. Wherein, Var (-) represents a variance calculation function, Cov (-) represents a covariance calculation function, and the definition formula is:
. E (-) represents the desired (i.e., mean) computation function.
Thus, random variablesXAndKthe calculation process of the correlation coefficient is summarized as follows:
from the above, the mean deviation ratio Para and the correlation coefficient are calculated
The method can perform enhanced self-adaptive preprocessing aiming at the discrete sequence, and reduce the influence of the noise signal in the initial stage on the process parameter estimation.
Optionally, the determining the magnitude of the noise signal according to the first parameter and the second parameter includes:
according to a first predetermined formula
Calculating a magnitude Q of the noise signal, wherein Para represents the first parameter,
representing the second parameter.
Optionally, the determining a posteriori estimated values of the second variables based on the magnitude of the noise signal includes:
and substituting the magnitude of the noise signal and the predetermined initial parameters of the Kalman equation into the predetermined Kalman filtering equation to obtain the posterior estimation values of the second variable at different sampling moments.
When a preset sliding window is moved from the sampling start time of the first variable according to a preset step length, and after the sliding window is moved once, the first parameter and the second parameter of the first variable are calculated according to the measured value in the sliding window, the magnitude of the noise signal (namely, the magnitude of the noise signal at different sampling times) can be obtained according to the first parameter and the second parameter obtained each time
) And substituting the magnitude of the noise signal at each moment and the initial parameters of the predetermined Kalman filtering equation into the predetermined Kalman filtering equation to obtain the posterior estimation values of the second variable at different sampling moments.
In addition, the kalman filter equation includes five equations as follows:
the state prediction equation:
， A
B=0，
adopting the interval time for the presetting, wherein the state prediction equation is used for estimating the current state by adopting the state at the last moment;
mean square error prediction equation:
，
representing the magnitude of the noise signal at the kth sampling instant; the mean square error prediction equation is used for estimating the current state error by using the error at the previous moment;
filter gain equation:
，
) A covariance matrix representing the noise of the sensor measurement at the kth sampling time, an
) Is a known amount; wherein, the filter gain equation is used for measuring the accuracy of the measured value;
the filter estimation equation:
，
，
,
,
represents the magnitude of the sensor measurement noise at the kth sampling time, and
is a known amount; the filtering estimation equation is used for acquiring a state variable at the current moment;
the state iteration equation:
(ii) a Wherein the state iteration equation is used to update the error term based on the current state variable.
Wherein, the state variable x (k) = in the above kalman filter equation
V (k) represents the first variable described above,
represents the second variable mentioned above, the above
Representing an a priori estimate of the state variable at the kth sampling instant,
representing the a posteriori estimate of the state variable at the kth sampling instant,
a covariance matrix representing the a posteriori estimate of the kth sampling instant,
a covariance matrix representing the prior estimate at the kth sampling instant.
E.g. k =0, i.e. the 0 th sampling instant, order
= 
Wherein, in the step (A),
i.e. the measured value of the first variable at the 0 th sampling instant,
taking a predetermined value; and, order
Respectively taking predetermined values, namely, predetermined initial parameters of a Kalman filtering equation:
、
。
based on the above, when k =1, i.e. the 1 st sampling instant, the equation of state prediction can be used
(ii) a Derived from the mean square error prediction equation
(ii) a From the filter gain equation
(ii) a Derived from a filter estimation equation
Wherein, in the step (A),
，
(ii) a From the state iteration equation
。
When k =2, i.e. the 2 nd sampling instant, the equation of state prediction can be used
(ii) a Root of herbaceous plantObtained from the mean square error prediction equation
(ii) a From the filter gain equation
(ii) a Derived from a filter estimation equation
Wherein, in the step (A),
，
(ii) a From the state iteration equation
。
Similarly, iteration is carried out by using a Kalman filtering equation at each subsequent sampling moment according to the method, so that the method can obtain
Wherein, in the step (A),
=
，
a posteriori estimates representing the first variable mentioned above,
the posterior estimated values of the second variable are shown, so that the posterior estimated values of the second variable at different sampling moments can be finally obtained.
Optionally, the determining process of the kalman filter equation includes:
determining a random state space equation according to the functional relation between the first variable and the second variable;
and determining the Kalman filtering equation based on the random state space equation.
In a first aspect, the process of determining the random state space equation according to the functional relationship between the first variable and the second variable is as follows:
the functional relationship between the first variable and the second variable may be converted to a linear form. For a linear signal, the sequential expression is: first expression
The variable to be found (i.e. the second variable) is typically included in the parameter M reflecting the growth rate and the parameter L reflecting the bias condition.
Discretizing the first expression to obtain a second expression:
. Assume state variable x (k) =
Let us order
，
Is far less than
The increment of (c). Wherein, for linear signals, the slope change of the previous sampling instant and the next sampling instant is small within a short time, so
Is a very small quantity, i.e.
The table indicates in a physical sense that the rate of increase (or decrease) of the measurement signal with time is substantially constant.
Order to
Then the third expression is obtained
And the fourth expression
。Wherein measurement noise is usually introduced during the measurement process, and
if v is a true value without noise, v is set
Representing the measurement noise, and y (k) representing the true value superimposed noise, i.e., the observation vector, the fifth expression y (k) = can be obtained
v
。
Wherein the content of the first and second substances,
therefore, the above-described third expression can be converted into a sixth expression:
. Thus, the expression is given by the sixth expression
And a fourth expression
A seventh expression can be derived: x is the number of
Wherein, in the step (A),
，x
，x
。
in addition, according to a fifth expression y (k) =
v
An eighth expression can be obtained: y (k) =
v
。
So far, a random state space equation is obtained
Wherein, in the step (A),
。
in a second aspect, the process of determining the kalman filter equation from the stochastic state space equation is as follows:
after the random state space equation is established, a linear recursive fitting needs to define a priori and posterior estimates and their errors. Wherein, define
Representing the estimated value produced from the last iteration calculation, called prior estimation, with the error of e
(ii) a Definition of
Representing an estimate produced from the current calculation, called a posteriori estimate, with an error of
。
The covariance matrix defining the a priori estimation error is
Then, then
Defining a covariance matrix of a posteriori estimation errors of
Then, then
。
The objective function of the state space linear recursive fitting model is to give a state observation vector y (k) at the k moment under the condition that the system structure is known, and to obtain the optimal estimation of the system state vector at the k moment
So that
And minimum.
First, according to the requirements
Calculate the k time instant (i.e. the k sample time)Moment) a priori estimate of
. Wherein an a priori estimate at the time of computation k (i.e. at
) When a corrected posterior estimated value should be used, i.e.
. Since here, a linear recursive fitting is performed, it is possible to obtain
. Wherein, the random state space equation obtained in the foregoing is:
thus, can obtain
And can then determine
B =0 (indicating no intervention of external control factors).
But because of
Presence of a prior estimation error, covariance matrix
The recursive formula of (A) requires the addition of noise interference Q (k), and Q (k) is of the order of
) In which is shown in
As a function of absolute value. Namely, the method comprises the following steps:
=A
。
then, the estimation can be based on a priori
Computing an estimate of an observation vector at time k
I.e. define
Wherein, in the step (A),
。
again, the measured value can be calculated
And the estimated value
Difference to correct the a priori estimate
To obtain a posterior estimate
I.e. by
. Wherein the content of the first and second substances,
，
then can obtain
And can further obtain
=
And then obtain
(I-
)
-
. Where v (k) is the sensor measurement noise, the magnitude is generally given by the sensor manufacturer, and I is the identity matrix.
And the foregoing has been
Thus, can obtain
=[
H]
+
Wherein, in the step (A),
is the covariance of v (k).
Thirdly, the model estimation principle is to make the optimal state estimation
Minimum, then order
Obtaining:
thereby will be
Substitution into
The optimum estimated state can be obtained
Comprises the following steps:
。
to this end, the five equations included in the kalman filter equation have been derived.
In addition, it should be further noted that the five equations included in the kalman filter equation (which may also be referred to as 5 core equations included in the enhanced adaptive recursive fitting algorithm) are divided into a time information update equation and a measurement parameter update equation.
Specifically, the time information parameter update equation includes the above-described state prediction equation (i.e., the equation of state
) And the mean square error prediction equation (i.e.
). The measurement information parameter update equation includes the filter gain equation(s) ((
) And filter estimation equation (i.e.
) And iterative equations of state (i.e.
）。
In summary, the specific implementation of the process parameter estimation method according to the embodiment of the present application may be as follows:
for example, in a monitoring system, the law of variation of the activation source signal u over time t is exponential (ideally), i.e. it is determined
Where T is the e-times period of the measurement signal (i.e., the start source signal u), the magnitude of which reflects the operating state of the system. In the actual signal monitoring process, measurement noise and other interference are usually introduced, the noise distribution generally follows gaussian distribution or poisson distribution, and if the noise is N, the expression of the measurement signal is:
wherein
，
In the true value, the value of,
，
in order to be able to measure the value,
，
is noise. Simplifying the expression for the measurement signal then yields:
further, it is possible to obtain:
wherein, in the step (A),
is equivalent noise.
Therefore, in the monitoring system, the monitoring amount can be determined according to the monitoring amount
And solving the posterior estimated value of the quantity T to be solved. From the functional relationship between the monitored quantity and the quantity to be determined
Can obtain
As a first variable in the embodiments of the present application,
is the second variable in the examples of this application.
Thus, the process of determining a posteriori estimates of the pending quantity T may include steps L1 through L5 as described below.
Step L1: according to a preset sampling interval time, collecting
And then calculating
Wherein the measured values are collected
May be as shown in the first graph 201 and the third graph 203 of figure 2,
may be as shown in the second graph 202 and the fourth graph 204 of fig. 2;
step L2: computing
A differential signal of a discrete sequence of measurements of (a);
step L3: starting from the sampling starting moment, moving a preset sliding window according to a preset step length, and after moving the sliding window once, moving the sliding window according to the position in the sliding window
Calculating a first parameter (i.e. the deviation average ratio Para) and a second parameter (i.e. the correlation coefficient) from the difference signal obtained from the set of measurement values
) Obtaining the deviation average ratio Para and the correlation coefficient
Values at different sampling moments, as shown in fig. 3; further, the magnitude of the noise signal at different sampling moments can be obtained;
step L4: substituting the magnitude of the noise signal and the predetermined initial parameters of the Kalman equation into the predetermined Kalman filtering equation to obtain
Posterior estimates at different sampling times;
step L5: according to
The a posteriori estimates at different sampling instants result in a posteriori estimate of T at different sampling instants, as shown in the fifth diagram 401 in fig. 4.
In addition, in order to verify the effectiveness of the process parameter estimation method provided by the embodiment of the application, other signal filtering or curve fitting methods are adopted to determine the posterior estimation value of the T of the reactor monitoring system, and the methods mainly comprise a direct estimation method, a random sampling consensus algorithm, a least power adaptive filtering algorithm, a maximum likelihood algorithm, a kalman filtering algorithm and the like. Specifically, as shown in fig. 4, a sixth graph 402 shows a posterior estimate of T determined by a direct estimation method, a seventh graph 403 shows a posterior estimate of T determined by a random sample consensus algorithm, an eighth graph 404 shows a posterior estimate of T determined by a least-squares adaptive filtering algorithm, a ninth graph 405 shows a posterior estimate of T determined by a maximum likelihood algorithm, and a tenth graph 406 shows a posterior estimate of T determined by a kalman filtering algorithm.
In the simulation experiment, the calculation precision (error), the response time and the algorithm running time are used as evaluation indexes for evaluating the performance of the estimation method. The calculation precision is embodied in a percentage error form and is a basic requirement on an algorithm; the response time is the time required for the output of the requested quantity to stabilize. The calculation accuracy was specified to be 1.00% (in percent error), and the response time and run time results for the six comparison algorithms are shown in table 1. Wherein the calculation precision of the minimum power self-adaptive filtering algorithm cannot meet the specified requirement.
TABLE 1 comparison of computational accuracy and response time for various signal processing algorithms
As can be seen from table 1 and fig. 4, the process parameter estimation method provided in the embodiment of the present application achieves the same calculation accuracy with the shortest response time and the best effect.
Referring to fig. 5, a block diagram of a process parameter estimation apparatus 500 according to an embodiment of the present disclosure may include the following modules:
the acquisition module 501 is configured to acquire a measurement value of a first variable according to a preset sampling interval time;
a parameter obtaining module 502, configured to obtain, according to the measured value, a first parameter and a second parameter of the first variable, where the first parameter is used to represent a degree of influence of a noise signal on an increment of the first variable in the preset sampling interval time, and the second parameter is used to represent a degree of change of the measured value with time;
a noise magnitude determination module 503, configured to determine a magnitude of the noise signal according to the first parameter and the second parameter;
a first estimation module 504 for determining a posteriori estimates of a second variable based on the magnitude of the noise signal;
wherein the first variable has a functional relationship with the second variable.
Optionally, the apparatus further comprises:
the variable acquisition module is used for acquiring the monitoring quantity and the quantity to be calculated of the system to be monitored;
a relationship obtaining module, configured to obtain a linear function relationship expression between the monitored quantity and the quantity to be solved, where a dependent variable of the linear function relationship expression includes the monitored quantity, and a coefficient of an independent variable of the linear function relationship expression includes the quantity to be solved;
and the variable determining module is used for determining the dependent variable of the linear function relational expression as the first variable and determining the coefficient of the independent variable of the linear function relational expression as the second variable.
Optionally, the apparatus further comprises:
and the second estimation module is used for determining the posterior estimation value of the quantity to be solved according to the posterior estimation value of the second variable.
Optionally, the parameter obtaining module is specifically configured to:
moving a preset sliding window according to a preset step length from the sampling start time of the first variable, and calculating the first parameter and the second parameter of the first variable according to the measured value in the sliding window after moving the sliding window once;
the preset step length comprises a preset number of sampling points.
Optionally, when the parameter obtaining module calculates the first parameter of the first variable according to the measured value in the sliding window, the parameter obtaining module is specifically configured to:
determining a discrete sequence of differential signals, wherein the discrete sequence comprises the measurements within the sliding window;
calculating the mean and standard deviation of the differential signal;
and calculating the ratio of the standard deviation to the average value to obtain the first parameter.
Optionally, when the parameter obtaining module calculates the second parameter of the first variable according to the measured value in the sliding window, the parameter obtaining module is specifically configured to:
calculating a correlation coefficient of a discrete sequence comprising the measurement values within the sliding window with a sampling time of the measurement values and determining the correlation coefficient as the second parameter.
Optionally, the noise level determination module is specifically configured to:
according to a first predetermined formula
Calculating a magnitude Q of the noise signal, wherein Para represents the first parameter,
representing the second parameter.
Optionally, the first estimating module is specifically configured to:
and substituting the magnitude of the noise signal and the predetermined initial parameters of the Kalman equation into the predetermined Kalman filtering equation to obtain the posterior estimation values of the second variable at different sampling moments.
Optionally, the apparatus further comprises:
the state space equation determining module is used for determining a random state space equation according to the functional relation between the first variable and the second variable;
and the Kalman filtering equation determining module is used for determining the Kalman filtering equation based on the random state space equation.
As can be seen from the above, embodiments of the present application estimate the magnitude of the noise signal based on the measured value of the first variable, thereby determining an a posteriori estimate of a second variable that is functionally related to the first variable based on the magnitude of the noise signal. Therefore, even if the system is unstable in the initial stage, the magnitude of the system noise can be accurately estimated, and further, the process parameters can be more accurately estimated (namely, the posterior estimation value of the second variable is more accurately determined) based on the estimated noise magnitude. Therefore, the embodiment of the application can relieve the influence of system noise on the process parameter estimation, thereby achieving higher estimation precision and shortening the response time for achieving the same precision.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
An embodiment of the present application further provides an electronic device, including:
one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the electronic device to perform methods as described herein.
Embodiments of the present application also provide one or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause the processors to perform the methods of embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method and the device for displaying the cover picture provided by the application are introduced in detail, a specific example is applied in the text to explain the principle and the implementation of the application, and the description of the embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method of process parameter estimation, the method comprising:
collecting a measured value of a first variable according to a preset sampling interval time;
acquiring a first parameter and a second parameter of the first variable according to the measured value, wherein the first parameter is used for representing the influence degree of a noise signal on the increment of the first variable in the preset sampling interval time, and the second parameter is used for representing the change degree of the measured value along with the time;
determining the magnitude of the noise signal according to the first parameter and the second parameter;
determining a posteriori estimate of a second variable based on the magnitude of the noise signal;
wherein the first variable has a functional relationship with the second variable;
determining the magnitude of the noise signal according to the first parameter and the second parameter includes:
according to a first predetermined formula
Calculating a magnitude Q of the noise signal, wherein Para represents the first parameter,
representing the second parameter;
determining a posteriori estimate of a second variable based on the magnitude of the noise signal, comprising:
and substituting the magnitude of the noise signal and the predetermined initial parameters of the Kalman equation into the predetermined Kalman filtering equation to obtain the posterior estimation values of the second variable at different sampling moments.
2. The method of process parameter estimation according to claim 1, wherein prior to collecting the measured value of the first variable at the predetermined sampling interval, the method further comprises:
acquiring the monitoring quantity and the quantity to be calculated of a system to be monitored;
acquiring a linear function relation expression between the monitored quantity and the quantity to be solved, wherein a dependent variable of the linear function relation expression comprises the monitored quantity, and a coefficient of an independent variable of the linear function relation expression comprises the quantity to be solved;
and determining the dependent variable of the linear function relational expression as the first variable, and determining the coefficient of the independent variable of the linear function relational expression as the second variable.
3. The method of claim 2, wherein after determining the a posteriori estimate of the second variable based on the magnitude of the noise signal, the method further comprises:
and determining the posterior estimated value of the quantity to be solved according to the posterior estimated value of the second variable.
4. The method of claim 1, wherein said obtaining a first parameter and a second parameter of the first variable from the measured value comprises:
moving a preset sliding window according to a preset step length from the sampling start time of the first variable, and calculating the first parameter and the second parameter of the first variable according to the measured value in the sliding window after moving the sliding window once;
the preset step length comprises a preset number of sampling points.
5. The process parameter estimation method of claim 4, wherein the process of calculating the first parameter of the first variable from the measurements within the sliding window comprises:
determining a discrete sequence of differential signals, wherein the discrete sequence comprises the measurements within the sliding window;
calculating the mean and standard deviation of the differential signal;
and calculating the ratio of the standard deviation to the average value to obtain the first parameter.
6. The process parameter estimation method of claim 4, wherein the process of calculating the second parameter of the first variable from the measured values within the sliding window comprises:
calculating a correlation coefficient of a discrete sequence comprising the measurement values within the sliding window with a sampling time of the measurement values and determining the correlation coefficient as the second parameter.
7. The method of process parameter estimation according to claim 1, wherein the determining of the kalman filter equation comprises:
determining a random state space equation according to the functional relation between the first variable and the second variable;
and determining the Kalman filtering equation based on the random state space equation.
8. A process parameter estimation apparatus to which the process parameter estimation method of any one of claims 1 to 7 is applied, the apparatus comprising:
the acquisition module is used for acquiring the measured value of the first variable according to the preset sampling interval time;
a parameter obtaining module, configured to obtain a first parameter and a second parameter of the first variable according to the measured value, where the first parameter is used to indicate a degree of influence of a noise signal on an increment of the first variable in the preset sampling interval time, and the second parameter is used to indicate a degree of change of the measured value with time;
a noise magnitude determination module, configured to determine a magnitude of the noise signal according to the first parameter and the second parameter;
a first estimation module for determining a posteriori estimate of a second variable based on the magnitude of the noise signal;
wherein the first variable has a functional relationship with the second variable.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the process parameter estimation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the process parameter estimation method according to one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110924502.3A CN113758503B (en) | 2021-08-12 | 2021-08-12 | Process parameter estimation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110924502.3A CN113758503B (en) | 2021-08-12 | 2021-08-12 | Process parameter estimation method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113758503A CN113758503A (en) | 2021-12-07 |
| CN113758503B true CN113758503B (en) | 2022-03-18 |
Family
ID=78789129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110924502.3A Active CN113758503B (en) | 2021-08-12 | 2021-08-12 | Process parameter estimation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113758503B (en) |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5206601A (en) * | 1992-04-29 | 1993-04-27 | Elanix Inc. | Order statistic signal processor for estimating values of signal parameters and demodulating amplitude-, phase-, and frequency-modulated signals |
| CN1458500A (en) * | 2003-06-17 | 2003-11-26 | 北京交通大学 | Dynamic measurement time domain compensation method |
| WO2006050409A1 (en) * | 2004-11-01 | 2006-05-11 | University Of Florida Research Foundation, Inc. | Methods for machining process parameter estimation and systems thereof |
| CN101542901A (en) * | 2007-01-23 | 2009-09-23 | 株式会社石田 | Noise removing device, weight measuring device, noise removing method, and digital filter designing method |
| CN103092078A (en) * | 2013-01-07 | 2013-05-08 | 北京中医药大学 | Whole-process optimizing method of multi-stage batch production process |
| CN104253776A (en) * | 2013-06-26 | 2014-12-31 | 英特尔Ip公司 | Method and device for processing signal based on first and second covariance measure |
| JP2015052491A (en) * | 2013-09-06 | 2015-03-19 | 沖電気工業株式会社 | Signal processing apparatus, signal processing method and computer program |
| CN105785324A (en) * | 2016-03-11 | 2016-07-20 | 西安电子科技大学 | MGCSTFT-based chirp signal parameter estimation method |
| JP2016143959A (en) * | 2015-01-30 | 2016-08-08 | 富士通株式会社 | Estimation device and estimation method |
| CN106814608A (en) * | 2016-12-08 | 2017-06-09 | 浙江中控软件技术有限公司 | PREDICTIVE CONTROL adaptive filter algorithm based on Posterior probability distribution |
| CN107290688A (en) * | 2017-08-24 | 2017-10-24 | 合肥工业大学 | A kind of lithium battery SOC methods of estimation based on adaptive fuzzy Kalman filtering |
| CN108696305A (en) * | 2018-04-17 | 2018-10-23 | 东南大学 | High-precision frequency deviation measurement method suitable for LTE-A MIMO signal analysis systems |
| CN109061686A (en) * | 2018-07-09 | 2018-12-21 | 太原理工大学 | Adaptive multipath estimation method based on recurrence broad sense maximum cross-entropy |
| CN109508510A (en) * | 2018-12-20 | 2019-03-22 | 国网河南省电力公司焦作供电公司 | A kind of rubidium atomic clock parameter estimation algorithm based on improved Kalman filtering |
| WO2019075014A1 (en) * | 2017-10-12 | 2019-04-18 | Diversey, Inc. | Filtered and integrated sensor measurement for process condition determination and method thereof |
| JP6581276B1 (en) * | 2018-10-18 | 2019-09-25 | 株式会社ショーワ | State quantity estimation apparatus, control apparatus, and state quantity estimation method |
| CN110532621A (en) * | 2019-07-30 | 2019-12-03 | 北京航空航天大学 | A kind of rudders pneumatic power parameter on-line identification method |
| CN110596593A (en) * | 2019-08-26 | 2019-12-20 | 浙江大学 | Lithium ion battery SOC estimation method based on intelligent adaptive extended Kalman filtering |
| CN112487708A (en) * | 2020-11-24 | 2021-03-12 | 山西三友和智慧信息技术股份有限公司 | Resistance spot welding quality prediction method based on ensemble learning |
| CN112883787A (en) * | 2021-01-14 | 2021-06-01 | 中国人民解放军陆军勤务学院 | Short sample low-frequency sinusoidal signal parameter estimation method based on spectrum matching |
| CN112969967A (en) * | 2018-11-07 | 2021-06-15 | Asml荷兰有限公司 | Determining corrections to a process |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7369697B2 (en) * | 2004-06-17 | 2008-05-06 | Intel Corporation | Process variable of interest monitoring and control |
-
2021
- 2021-08-12 CN CN202110924502.3A patent/CN113758503B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5206601A (en) * | 1992-04-29 | 1993-04-27 | Elanix Inc. | Order statistic signal processor for estimating values of signal parameters and demodulating amplitude-, phase-, and frequency-modulated signals |
| CN1458500A (en) * | 2003-06-17 | 2003-11-26 | 北京交通大学 | Dynamic measurement time domain compensation method |
| WO2006050409A1 (en) * | 2004-11-01 | 2006-05-11 | University Of Florida Research Foundation, Inc. | Methods for machining process parameter estimation and systems thereof |
| CN101542901A (en) * | 2007-01-23 | 2009-09-23 | 株式会社石田 | Noise removing device, weight measuring device, noise removing method, and digital filter designing method |
| CN103092078A (en) * | 2013-01-07 | 2013-05-08 | 北京中医药大学 | Whole-process optimizing method of multi-stage batch production process |
| CN104253776A (en) * | 2013-06-26 | 2014-12-31 | 英特尔Ip公司 | Method and device for processing signal based on first and second covariance measure |
| JP2015052491A (en) * | 2013-09-06 | 2015-03-19 | 沖電気工業株式会社 | Signal processing apparatus, signal processing method and computer program |
| JP2016143959A (en) * | 2015-01-30 | 2016-08-08 | 富士通株式会社 | Estimation device and estimation method |
| CN105785324A (en) * | 2016-03-11 | 2016-07-20 | 西安电子科技大学 | MGCSTFT-based chirp signal parameter estimation method |
| CN106814608A (en) * | 2016-12-08 | 2017-06-09 | 浙江中控软件技术有限公司 | PREDICTIVE CONTROL adaptive filter algorithm based on Posterior probability distribution |
| CN107290688A (en) * | 2017-08-24 | 2017-10-24 | 合肥工业大学 | A kind of lithium battery SOC methods of estimation based on adaptive fuzzy Kalman filtering |
| WO2019075014A1 (en) * | 2017-10-12 | 2019-04-18 | Diversey, Inc. | Filtered and integrated sensor measurement for process condition determination and method thereof |
| CN108696305A (en) * | 2018-04-17 | 2018-10-23 | 东南大学 | High-precision frequency deviation measurement method suitable for LTE-A MIMO signal analysis systems |
| CN109061686A (en) * | 2018-07-09 | 2018-12-21 | 太原理工大学 | Adaptive multipath estimation method based on recurrence broad sense maximum cross-entropy |
| JP6581276B1 (en) * | 2018-10-18 | 2019-09-25 | 株式会社ショーワ | State quantity estimation apparatus, control apparatus, and state quantity estimation method |
| CN112969967A (en) * | 2018-11-07 | 2021-06-15 | Asml荷兰有限公司 | Determining corrections to a process |
| CN109508510A (en) * | 2018-12-20 | 2019-03-22 | 国网河南省电力公司焦作供电公司 | A kind of rubidium atomic clock parameter estimation algorithm based on improved Kalman filtering |
| CN110532621A (en) * | 2019-07-30 | 2019-12-03 | 北京航空航天大学 | A kind of rudders pneumatic power parameter on-line identification method |
| CN110596593A (en) * | 2019-08-26 | 2019-12-20 | 浙江大学 | Lithium ion battery SOC estimation method based on intelligent adaptive extended Kalman filtering |
| CN112487708A (en) * | 2020-11-24 | 2021-03-12 | 山西三友和智慧信息技术股份有限公司 | Resistance spot welding quality prediction method based on ensemble learning |
| CN112883787A (en) * | 2021-01-14 | 2021-06-01 | 中国人民解放军陆军勤务学院 | Short sample low-frequency sinusoidal signal parameter estimation method based on spectrum matching |
Non-Patent Citations (4)
| Title |
|---|
| Estimation of the parameters of autoregressive signals from colored noise-corrupted measurements;W.-X. Zheng;《IEEE Signal Processing Letters》;20000731;第7卷(第7期);第201-204页 * |
| Properties of one-step prediction observers (identifiers) for system parameter identification;R.N.K. Loh et.al;《6th International Conference on Signal Processing》;20021231;第96-101页 * |
| 自适应Kalman滤波的运动物体跟踪算法研究;张秀杰等;《燕山大学学报》;20120930;第36卷(第5期);第428-432页 * |
| 非侵入式负荷识别边缘计算颜色编码研究;陈军锋等;《仪器仪表学报》;20200930;第41卷(第9期);第12-19页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113758503A (en) | 2021-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107085140B (en) | Nonequilibrium system frequency estimating methods based on improved SmartDFT algorithm | |
| CN103927436A (en) | Self-adaptive high-order volume Kalman filtering method | |
| CN109508510B (en) | Improved Kalman filtering-based rubidium atomic clock parameter estimation algorithm | |
| CN107783938B (en) | Method for estimating instantaneous rotating speed of rotating equipment | |
| CN114124033A (en) | Kalman filter implementation method, device, storage medium and equipment | |
| CN114460527B (en) | Correlation degree continuation Hilbert phase-shifting electronic transformer calibrator source tracing method and system | |
| CA2456855C (en) | Method and apparatus for reducing skew in a real-time centroid calculation | |
| CN114235072B (en) | Zero-crossing detection-based Coriolis flowmeter phase difference calculation method | |
| KR101944429B1 (en) | Method for frequency analysis and apparatus supporting the same | |
| CN113758503B (en) | Process parameter estimation method and device, electronic equipment and storage medium | |
| WO2004090782A1 (en) | Accurate linear parameter estimation with noisy inputs | |
| WO2018014631A1 (en) | Method and apparatus for determining peak power, peak-to-average power ratio | |
| CN104407366A (en) | Pseudo-range smooth processing method | |
| CN114614797B (en) | Adaptive filtering method and system based on generalized maximum asymmetric correlation entropy criterion | |
| CN115498980A (en) | Recursive minimum p-order adaptive filtering positioning method based on M estimation | |
| CN116176531A (en) | Method and device for determining performance index of opening degree adjustment and storage medium | |
| RU2610831C1 (en) | Method for estimating parameters of object's trajectory | |
| US8326907B2 (en) | Method for establishing a simulating signal suitable for estimating a complex exponential signal | |
| CN112929214A (en) | Model construction method, device, equipment and storage medium | |
| CN107506779B (en) | Estimation method and system for water content of plant stems | |
| CN113299284A (en) | Speech recognition device, method, equipment and medium based on adaptive filtering | |
| Harvey | The local quadratic trend model | |
| CN110858309A (en) | Multi-reference time clock weighting synthesis method | |
| Cardona-Morales et al. | Instantaneous frequency estimation and order tracking based on Kalman filters | |
| Liu et al. | Modeling of mems gyroscope random error based on Kalman filter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |