CN107942304A - Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine - Google Patents
Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine Download PDFInfo
- Publication number
- CN107942304A CN107942304A CN201711117119.7A CN201711117119A CN107942304A CN 107942304 A CN107942304 A CN 107942304A CN 201711117119 A CN201711117119 A CN 201711117119A CN 107942304 A CN107942304 A CN 107942304A
- Authority
- CN
- China
- Prior art keywords
- support vector
- vector machine
- optimization
- drosophila
- function
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
Abstract
A most young waiter in a wineshop or an inn is optimized into the sea clutter optimal soft survey instrument and method of support vector machines based on drosophila the invention discloses a kind of, including radar, field intelligent instrument, control station, the spot database for storing data, optimal hard measurement host computer and forecast hard measurement value display instrument based on improved drosophila optimization algorithm optimization wavelet neural network, field intelligent instrument and control station are connected with radar, are connected with spot database；Optimal hard measurement host computer is connected with spot database and hard measurement value display instrument.Described optimizes a most young waiter in a wineshop or an inn into the optimal hard measurement host computer of support vector machines, including data preprocessing module, wavelet neural network module, model modification module based on drosophila, and provides a kind of flexible measurement method realized with soft measuring instrument.The present invention realizes the online optimal hard measurement of sea clutter, and randomness caused by overcoming human factor influences, and improves the stability of model prediction, reduces the possibility that model prediction is absorbed in local optimum.
Description
Technical Field
The invention relates to the field of optimal soft measuring instruments and methods, in particular to a fruit fly optimization algorithm-based optimal sea clutter soft measuring instrument and method for optimizing a least square support vector machine.
Background
In the field of radar, echo signals reflected from the surface of seawater are called sea clutter, and the sea clutter is related to various factors such as sea conditions, tides, radar parameters and the like. For coastal warning radars, ship-borne radars and other radars working in marine environments, the detection and tracking performance of sea targets are influenced by serious sea surface reflection echoes, the properties of sea clutter are mastered, and the establishment of an accurate sea clutter model is a premise for analyzing and improving the radar performance. The statistical properties of the sea clutter include amplitude properties and correlation properties. The correlation properties of the sea clutter include temporal correlation and spatial correlation. The time correlation is also called as inter-pulse correlation, reflects the fluctuation of the amplitude of the sea clutter with time, and can be equivalently represented by a power spectrum. The spatial correlation of sea clutter is divided into azimuth correlation and distance correlation. Sea clutter energy helps us to study the dynamics of the ocean, but if targets are to be detected from a sea clutter background, such as ice floes, ships, etc., it becomes a significant obstacle and must be suppressed as much as possible to reduce or eliminate these disturbances. The main purpose of the research on sea clutter is as follows: on one hand, the natural mechanism of the sea clutter is explained, and a reasonable model is provided; on the other hand, the method is to reduce the interference of the sea clutter on the detection target and find out how to extract the target signal submerged in the background of the strong sea clutter. The key to achieving the above purposes is the establishment of an accurate sea clutter model
Most of the research work on modeling and forecasting of the sea clutter in recent years is focused on the artificial neural network, and good effect is achieved. However, artificial neural networks also have their own drawbacks, such as overfitting, the number of nodes in the hidden layer, and poor parameter determination. Secondly, the data collected in the observation field also has certain uncertain errors due to noise, manual operation errors and the like, so that the forecasting model using the artificial neural network with strong certainty is generally not strong in popularization capability. Support vector machines, introduced by Vapnik in 1998, are widely used in pattern recognition, fitting and classification problems due to their good generalization ability. Since the standard support vector machine is sensitive to isolated points and noise points, a least squares support vector machine was proposed later. The least squares support vector machine is better able to process noisy sample data than the standard support vector machine, and is used here for modeling. The Fruit Fly Optimization Algorithm, namely, the Fruit Fly Optimization Algorithm, is a biological intelligent Optimization Algorithm, called FOA for short, which is proposed by professor Wen-Tsao Pan in Taiwan and deduced from Erlenmeyer based on foraging behavior of Fruit flies. The algorithm reduces the risk of trapping the search algorithm into a local optimal solution through the mutual influence among particles in the group, and has good global search performance. The drosophila optimization algorithm is used for searching the optimal parameter combination of the least square support vector machine so as to achieve the purpose of optimizing the model.
Disclosure of Invention
In order to overcome the defects of low measurement precision, low noise sensitivity and poor popularization performance of the conventional radar, the invention provides the optimal sea clutter soft measurement instrument and method based on the drosophila optimization algorithm optimization least square support vector machine, which have the advantages of online measurement, high calculation speed, automatic model update, strong noise resistance and good popularization performance.
The purpose of the invention is realized by the following technical scheme: the utility model provides a sea clutter optimal soft measuring instrument based on fruit bat optimization algorithm optimizes least square support vector machine, includes the radar, is used for measuring easy measureable variable's on-the-spot intelligent instrument, is used for measuring the control station of manipulated variable, deposits the on-the-spot database of data and sea clutter forecast soft measurement display, on-the-spot intelligent instrument, control station and propylene polymerization production process are connected, on-the-spot intelligent instrument, control station and on-the-spot database are connected, soft measuring instrument still includes the optimal soft measurement host computer based on fruit bat optimization algorithm optimizes least square support vector machine, on-the-spot database with the input of the optimal soft measurement host computer based on fruit bat optimization algorithm optimizes least square support vector machine is connected, the output of the optimal soft measurement host computer based on fruit bat optimization algorithm optimization support vector machine is connected with sea clutter soft measurement display, the optimal soft measurement host computer based on fruit bat optimization algorithm optimization least square support vector machine includes:
the data preprocessing module is used for preprocessing the model training samples input from the field database, centralizing the training samples, namely subtracting the average value of the samples, and then normalizing the training samples:
calculating an average value:
calculating the variance:
and (3) standardization:
wherein TX is a training sample, N is the number of training samples,is the mean of the training samples, X is the normalized training sample, σ x To calculate the variance.
And the least square support vector machine module adopts a least square support vector machine for modeling. Model ith normalized training sample X i Target output of O i The least squares support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
where R (w, ξ) is the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, xi = { xi = 1 ,…,ξ N Is the relaxation variable, w is the normal vector of the hyperplane of the least squares support vector machine, b is the corresponding offset, and ω is i I =1, \8230, N and gamma are the weight and penalty factors, respectively, of the least squares support vector machine, whereIs the relaxation variable xi i Estimate of standard deviation, constant c 1 ,c 2 Is generally taken as c 1 ＝2.5，c 2 =3, from which a normalized training sample X can be obtained i The output of (c) is:
wherein, K<·> is the kernel function of a least squares support vector machine, where K<·&Taking a linear kernel function; alpha is alpha m M =1, \8230, N is the corresponding lagrange multiplier.
The fruit fly optimization algorithm optimization module is used for optimizing punishment factors and error tolerance values of the least square support vector machine by adopting a fruit fly optimization algorithm, and comprises the following specific steps:
(1) determining optimization parameters of drosophila optimization algorithm as penalty factors and error capacities of least square support vector machine moduleLimit value, particle group individual number popsize, maximum cycle optimization number iter max And the initial position regions X _ axis, Y _ axis of the p-th particle.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, calculating the fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:
f p ＝1/(E p +1) (8)
in the formula, E p Is the error function of the least squares support vector machine model, expressed as:
in the formula (I), the compound is shown in the specification,is the predicted output of the least squares support vector machine model, O i Outputting a target of a least squares support vector machine model; n is the number of training samples;
(3) according to the following formula, the particles are searched,
in the formula, randomValue is the search distance;
(4) for the particle p, the distance Dist from the origin is estimated in advance, and the taste concentration determination value S is calculated as the reciprocal distance:
Dist i ＝(X i 2 +Y i 2 ) 1/2 (11)
S i ＝1/Dist i (12)
(5) judging taste concentration value S i A substitute taste concentration judgment function (or fitness function) for determining the taste concentration Smell of the individual positions of the drosophila i :
[bestSmell bestIndex]＝min(Smell) (13)
(6) Finding the individuals with the best taste concentration in the drosophila population, taking the minimum value:
[bestSmell bestIndex]＝min(Smell) (14)
(7) the optimal individual position and taste concentration values were recorded, at which time all drosophila individuals would fly visually to this position:
(8) judging whether the performance requirements are met, if so, finishing the optimization to obtain a group of optimized parameters of the least square support vector machine;
otherwise, returning to the step (3), and continuing to iterate and optimize until the maximum iteration number iter is reached max 。
As a preferred scheme, the optimal soft measurement upper computer based on the drosophila optimization algorithm optimized least squares support vector machine further comprises: and the model updating module is used for updating the model on line, inputting offline verification data into a training set regularly and updating the least square support vector machine model.
A sea clutter optimal soft measurement method based on a drosophila optimization algorithm optimization least square support vector machine comprises the following steps:
1) Selecting an operation variable and a readily measurable variable as the input of the model for the radar object according to characteristic analysis and climate analysis, wherein the operation variable and the readily measurable variable are obtained from a field database;
2) Preprocessing a model training sample input from a field database, centralizing the training sample, namely subtracting the average value of the sample, and then normalizing the training sample so that the average value is 0 and the variance is 1. The process is accomplished using the following algorithm:
2.1 Calculate the mean value:
2.2 Calculate variance:
2.3 Normalization:
wherein TX is a training sample, N is the number of training samples,is the mean of the training samples, and X is the normalized training sample.
3) And modeling the training sample transmitted from the data preprocessing module by adopting a least square support vector machine. Model training sample X i Target output of O i The least squares support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
where R (w, ξ) is the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, xi = { xi = 1 ,…,ξ N Is the relaxation variable, w is the normal vector of the hyperplane of the least squares support vector machine, b is the corresponding offset, and ω is i I =1, \8230, N and γ are the weight and penalty factors, respectively, of a least squares support vector machine, whereIs the relaxation variable xi i Estimate of standard deviation, constant c 1 ,c 2 Is generally taken as c 1 ＝2.5，c 2 =3, from which training sample X can be obtained i The output of (c) is:
wherein, K<·> is the kernel function of a least squares support vector machine, where K<·&Taking a linear kernel function; alpha (alpha) ("alpha") m M =1, \ 8230, N is the corresponding lagrange multiplier.
4) Optimizing punishment factors and error tolerance values of the least square support vector machine by adopting a drosophila optimization algorithm, and specifically comprising the following steps:
(1) determining optimization parameters of a drosophila optimization algorithm as a penalty factor and an error tolerance value of a least square support vector machine module, the individual number of particle swarm popsize and the maximum cycle optimization number iter max And the initial position regions X _ axis, Y _ axis of the p-th particle.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, calculating the fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:
f p ＝1/(E p +1) (8)
in the formula, E p Is the error function of the least squares support vector machine model, expressed as:
in the formula (I), the compound is shown in the specification,is the predicted output of the least squares support vector machine model, O i Outputting a target of a least squares support vector machine model; n is the number of training samples;
(3) according to the following formula, the particles are searched,
in the formula, randomValue is the search distance;
(4) for the particle p, the distance Dist from the origin is estimated in advance, and the taste concentration determination value S is calculated as the reciprocal distance:
Dist i ＝(X i 2 +Y i 2 ) 1/2 (11)
S i ＝1/Dist i (12)
(5) judging the taste concentration value S i A substitute taste concentration judgment function (or fitness function) for determining the taste concentration Smell of the individual positions of the drosophila i :
[bestSmell bestIndex]＝min(Smell) (13)
(6) Finding the individuals with the best taste concentration in the drosophila population, taking the minimum value:
[bestSmell bestIndex]＝min(Smell) (14)
(7) the optimal individual position and taste concentration values were recorded, at which time all drosophila individuals would fly visually to this position:
(8) judging whether the performance requirements are met, if so, finishing the optimization to obtain a group of optimized parameters of the least square support vector machine; otherwise, returning to the step (3), and continuing to iterate and optimize until the maximum iteration number iter is reached max 。
As a preferred solution: the soft measurement method further comprises the following steps: 5) And inputting the offline experimental data into a training set regularly, and updating the least square support vector machine model.
The technical conception of the invention is as follows: the online optimal soft measurement is carried out on the sea clutter, the defects that the existing sea clutter measuring instrument is poor in stability and prone to falling into local optimization are overcome, the fruit fly optimization algorithm is introduced to carry out automatic optimization on the least square support vector machine model, and the parameters of the least square support vector machine are not required to be adjusted for multiple times through artificial experience, so that the optimal soft measurement result is obtained. Compared with the existing sea clutter soft measurement model, the model has the following advantages: modeling is carried out through a least square support vector machine model, and high forecasting precision is achieved; the existing model parameters are generally determined through experience of operators, the existing model parameters have certain limitation and uncertainty, once the values are not properly taken, oscillation of prediction output of the model and a large prediction error can be caused, and the model automatically optimizes the parameters of the model through a drosophila optimization algorithm to obtain an optimal soft measurement model.
The invention has the following beneficial effects: the online optimal soft measurement of the sea clutter is realized, the random influence caused by human factors is overcome, the stability of model prediction is improved, and the possibility that the model prediction falls into local optimization is reduced.
Drawings
FIG. 1 is a schematic diagram of the basic structure of a fruit fly optimization algorithm-based optimal soft measurement instrument and method for optimizing the sea clutter modeling process of a least square support vector machine;
FIG. 2 is a schematic structural diagram of an optimal soft measurement upper computer based on a least square support vector machine optimized by a drosophila optimization algorithm;
Detailed Description
The invention is further described below with reference to the accompanying drawings. The present embodiments are to be considered as illustrative and not restrictive, and all changes and modifications that come within the spirit of the invention and the scope of the appended claims are intended to be embraced therein.
Example 1
Referring to fig. 1 and 2, the optimal soft measuring instrument for the sea clutter based on the least square support vector optimization algorithm optimization least square support vector machine comprises a radar 1, an on-site intelligent instrument 2 used for measuring variables easy to measure, a control station 3 used for measuring operation variables, an on-site database 4 used for storing data and a sea clutter soft measurement value display instrument 6, wherein the on-site intelligent instrument 2 and the control station 3 are connected with the radar 1, the on-site intelligent instrument 2 and the control station 3 are connected with the on-site database 4, the soft measuring instrument further comprises an optimal soft measurement upper computer 5 of the least square support vector optimization algorithm optimization least square support vector machine, the on-site database 4 is connected with the input end of the optimal soft measurement upper computer 5 of the least square support vector optimization algorithm optimization least square support vector machine based on the drosophila optimization algorithm, and the output end of the optimal soft measurement upper computer 5 of the least square support vector optimization algorithm optimization least square support vector machine based on the drosophila optimization algorithm optimization least square support vector machine is connected with the sea clutter soft measurement value display instrument 6. The field intelligent instrument 2 measures the easily-measured variable of the radar object and transmits the easily-measured variable to the field database 4; the control station 3 controls the manipulated variables of the radar object, and transmits the manipulated variables to the field database 4. The variable data recorded in the field database 4 is used as the input of the optimal soft measurement upper computer 5 based on the least square support vector machine optimized by the drosophila optimization algorithm, and the soft measurement value display instrument 6 is used for displaying the output, namely the soft measurement value, of the optimal soft measurement upper computer 5 based on the least square support vector machine optimized by the drosophila optimization algorithm. The optimal soft measurement upper computer 5 based on the least square support vector machine optimized by the drosophila optimization algorithm comprises the following 4 parts:
a data preprocessing module 7, configured to preprocess model training samples input from the field database, centralize the training samples, that is, subtract an average value of the samples, and then normalize the training samples:
calculating an average value:
calculating the variance:
and (3) standardization:
wherein TX is a training sample, N is the number of training samples,is the mean of the training samples, X is the normalized training sample, σ x To calculate the variance.
And the least square support vector machine module 8 adopts a least square support vector machine to carry out modeling. Model ith normalized training sample X i Target output of O i The least squares support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
where R (w, ξ) is the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ = 1 ,…,ξ N Is the relaxation variable, w is the normal vector of the hyperplane of the least squares support vector machine, b is the corresponding offset, and ω is i I =1, \8230, N and γ are the weight and penalty factors, respectively, of a least squares support vector machine, whereIs a relaxation variable ξ i Estimate of standard deviation, constant c 1 ,c 2 Is generally taken as c 1 ＝2.5，c 2 =3, from which normalized training sample X can be obtained i The output of (c) is:
wherein, K<·> is the kernel function of a least squares support vector machine, where K<·&Taking a linear kernel function; alpha is alpha m M =1, \8230, N is the corresponding lagrange multiplier.
The drosophila optimization algorithm module 9 is used for optimizing punishment factors and error tolerance values of the least square support vector machine by adopting a drosophila optimization algorithm, and comprises the following specific steps:
(1) determining optimization parameters of a drosophila optimization algorithm as a penalty factor and an error tolerance value of a least square support vector machine module, the individual number of particle swarm popsize and the maximum cycle optimization number iter max And the initial position regions X _ axis, Y _ axis of the p-th particle.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, calculating the fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:
f p ＝1/(E p +1) (8)
in the formula, E p Is an error function of a least squares support vector machine model, expressed as:
in the formula (I), the compound is shown in the specification,is a least squares support vectorPredicted output of model, O i Outputting a target of a least squares support vector machine model; n is the number of training samples;
(3) according to the following formula, the particles are searched,
in the formula, randomValue is the search distance;
(4) for the particle p, the distance Dist from the origin is estimated in advance, and the taste concentration determination value S is calculated as the reciprocal distance:
Dist i ＝(X i 2 +Y i 2 ) 1/2 (11)
S i ＝1/Dist i (12)
(5) judging taste concentration value S i A substitute taste concentration judgment function (or fitness function) for determining the taste concentration Smell of the individual positions of the drosophila i :
[bestSmell bestIndex]＝min(Smell) (13)
(6) Finding the individuals with the best taste concentration in the drosophila population, taking the minimum value:
[bestSmell bestIndex]＝min(Smell) (14)
(7) the optimal individual position and taste concentration values were recorded, at which time all drosophila individuals would fly visually to this position:
(8) judging whether the performance requirements are met, if so, finishing the optimization to obtain a group of optimized parameters of the least square support vector machine; otherwise, returning to the step (3), and continuing the iteration optimization until the maximum iteration number iter is reached max 。
As a preferred scheme, the optimal soft measurement upper computer based on the least square support vector machine optimized by the drosophila optimization algorithm further comprises: and the model updating module 10 is used for updating the model on line, inputting offline experimental data into a training set regularly, and updating the least square support vector machine model.
Example 2
Referring to fig. 1 and 2, a method for optimal soft measurement of sea clutter based on a drosophila optimization algorithm optimization least square support vector machine comprises the following steps:
1) Selecting an operation variable and an easily-measured variable as input of a model for the radar object according to process analysis and operation analysis, wherein the operation variable and the easily-measured variable are obtained by a field database;
2) Preprocessing a model training sample input from a field database, centralizing the training sample, namely subtracting the average value of the sample, and then normalizing the training sample so that the average value is 0 and the variance is 1. The processing is accomplished using the following mathematical process:
2.1 Calculate the mean value:
2.2 Calculate variance:
2.3 Normalization:
wherein TX is a training sample, N is the number of training samples,is the mean of the training samples, X is the normalized training sample, σ x To calculate the variance.
3) And modeling the training sample transmitted from the data preprocessing module by adopting a least square support vector machine. Training after model standardizationSample X i Target output of O i The least squares support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
where R (w, ξ) is the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ = 1 ,…,ξ N Is the relaxation variable, w is the normal vector of the hyperplane of the least squares support vector machine, b is the corresponding offset, and ω is i I =1, \8230, N and γ are the weight and penalty factors, respectively, of a least squares support vector machine, whereIs a relaxation variable ξ i Estimate of standard deviation, constant c 1 ,c 2 Is usually taken as c 1 ＝2.5，c 2 =3, from which a normalized training sample X can be obtained i The output of (c) is:
wherein, K<·> is the kernel function of a least squares support vector machine, where K<·>, taking a linear kernel function; alpha is alpha m M =1, \8230, N is the corresponding lagrange multiplier.
4) Optimizing punishment factors and error tolerance values of the least square support vector machine by adopting a drosophila optimization algorithm, and specifically comprising the following steps:
(1) determining optimization parameters of a drosophila optimization algorithm as a penalty factor and an error tolerance value of a least square support vector machine module, the individual number of particle swarm popsize and the maximum cycle optimization number iter max And the initial position regions X _ axis, Y _ axis of the p-th particle.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, calculating the fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function f of the particle p as follows:
f p ＝1/(E p +1) (8)
in the formula, E p Is the error function of the least squares support vector machine model, expressed as:
in the formula (I), the compound is shown in the specification,is the predicted output of the least squares support vector machine model, O i Outputting a target of a least squares support vector machine model; n is the number of training samples;
(3) according to the following formula, the particles are searched,
in the formula, randomValue is the search distance;
(4) for the particle p, the distance Dist from the origin is estimated in advance, and the taste concentration determination value S is calculated as the reciprocal distance:
Dist i ＝(X i 2 +Y i 2 ) 1/2 (11)
S i ＝1/Dist i (12)
(5) judging the taste concentration value S i A substitute taste concentration judgment function (or fitness function) for calculating the taste concentration Smell of the individual positions of the fruit flies i :
[bestSmell bestIndex]＝min(Smell) (13)
(6) Finding the individuals with the best taste concentration in the drosophila population, taking the minimum value:
[bestSmell bestIndex]＝min(Smell) (14)
(7) the optimal individual position and taste concentration values were recorded, at which time all drosophila individuals would fly visually to this position:
(8) judging whether the performance requirements are met, if so, ending the optimization, and obtaining a group of optimized parameters of the least square support vector machine;
otherwise, returning to the step (3), and continuing the iteration optimization until the maximum iteration number iter is reached max 。
As a preferred solution: the soft measurement method further comprises the following steps: 4) And inputting the offline experimental data into a training set regularly, and updating the least square support vector machine model.
The method of the embodiment comprises the following specific implementation steps:
step 1: for the radar object 1, the manipulated variable and the easily measurable variable are selected as the inputs of the model based on the characteristic analysis and the climate analysis. The manipulated variables and easily measurable variables are obtained from the field database 4.
And 2, step: and sample data is preprocessed and completed by a data preprocessing module 7.
And step 3: an initial least squares support vector machine model 8 is established based on model training sample data. Input data is obtained as described in step 2 and output data is obtained from an off-line assay.
And 4, step 4: the parameters of the initial least squares support vector machine model are optimized by the drosophila optimization algorithm module 9.
And 5: the model updating module 10 periodically inputs offline test data into a training set, updates the least square support vector machine model, and optimizes the optimal soft measurement upper computer 5 of the least square support vector machine model based on the drosophila optimization algorithm to complete establishment.
Step 6: and the sea clutter soft measurement value display instrument 6 displays the output of the optimal soft measurement upper computer 5 based on the least square support vector machine model optimized by the drosophila optimization algorithm, and the optimal soft measurement of the sea clutter is displayed.
The optimal soft measurement upper computer based on the drosophila optimization minimum two-component support vector machine comprises a data preprocessing module, a wavelet neural network module and a model updating module, and provides a soft measurement method realized by a soft measurement instrument. The method realizes online optimal soft measurement of the sea clutter, overcomes the random influence caused by human factors, improves the stability of model prediction, and reduces the possibility that the model prediction falls into local optimization.
Claims (2)
1. A fruit fly optimization algorithm based sea clutter optimal soft measuring instrument for optimizing a least square support vector machine comprises a radar, a field intelligent instrument for measuring easily-measured variables, a control station for measuring operation variables, a field database for storing data and a sea clutter soft measuring value display instrument; the field intelligent instrument and the control station are connected with the radar, and the field intelligent instrument and the control station are connected with a field database, and the radar is characterized in that: the soft measuring instrument further comprises an optimal soft measuring upper computer based on a least square support vector machine optimized by a drosophila optimization algorithm, the field database is connected with the input end of the optimal soft measuring upper computer based on the least square support vector machine optimized by the drosophila optimization algorithm, and the output end of the optimal soft measuring upper computer based on the least square support vector machine optimized by the drosophila optimization algorithm is connected with a sea clutter soft measurement value display instrument; the optimal soft measurement upper computer based on the least square support vector machine optimized by the drosophila optimization algorithm comprises:
the data preprocessing module is used for preprocessing the model training samples input from the field database, centralizing the training samples, namely subtracting the average value of the samples, and then normalizing the training samples:
calculating an average value:
calculating the variance:
and (3) standardization:
wherein TX is a training sample, N is the number of training samples,is the mean of the training samples, X is the normalized training sample, σ x To calculate the variance.
And the least square support vector machine module adopts a least square support vector machine to carry out modeling. Let training sample X after model ith standardization i Target output of O i The least squares support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
where R (w, ξ) is the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ = 1 ,…,ξ N Is the relaxation variable, w is the normal vector of the hyperplane of the least squares support vector machine, b is the corresponding offset, and ω is i I =1, \8230, N and γ are the weight and penalty factors, respectively, of a least squares support vector machine, whereIs the relaxation variable xi i Estimate of standard deviation, constant c 1 C2 is usually taken as c 1 ＝2.5，c 2 =3, from which the ith normalized training sample X can be obtained i The output of (c) is:
wherein, K<·> is the kernel function of a least squares support vector machine, where K<·&Taking a linear kernel function; alpha is alpha m M =1, \ 8230, N is the corresponding lagrange multiplier.
The fruit fly optimization algorithm module is used for optimizing punishment factors and error tolerance values of the least square support vector machine by adopting a fruit fly optimization algorithm, and comprises the following specific steps:
(1) determining optimization parameters of the drosophila optimization algorithm as a penalty factor and an error tolerance value of a least square support vector machine module, the individual number of particle swarm popsize and the maximum cyclic optimization number iter max And the initial position regions X _ axis, Y _ axis of the p-th particle.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, calculating the fitness function through a corresponding error function, considering that the fitness of the particles with large errors is small, and expressing the fitness function f of the particles p as:
f p ＝1/(E p +1) (8)
in the formula (I), the compound is shown in the specification,E p is the error function of the least squares support vector machine model, expressed as:
in the formula (I), the compound is shown in the specification,is the predicted output of the least squares support vector machine model, O i Outputting a target of a least squares support vector machine model; n is the number of training samples;
(3) according to the following formula, the particles are searched,
in the formula, randomValue is the search distance;
(4) for the particle p, the distance Dist from the origin is estimated in advance, and the taste concentration determination value S is calculated as the reciprocal distance:
Dist i ＝(X i 2 +Y i 2 ) 1/2 (11)
S i ＝1/Dist i (12)
(5) judging taste concentration value S i A substitute taste concentration judgment function (or fitness function) for determining the taste concentration Smell of the individual positions of the drosophila i :
[bestSmell bestIndex]＝min(Smell) (13)
(6) Finding the individuals with the best taste concentration in the drosophila population, taking the minimum value:
[bestSmell bestIndex]＝min(Smell) (14)
(7) the optimal individual position and taste concentration values were recorded, at which time all drosophila individuals would fly visually to this position:
(8) judging whether the performance requirements are met, if so, ending the optimization, and obtaining a group of optimized parameters of the least square support vector machine; otherwise, returning to the step (3), and continuing the iteration optimization until the maximum iteration number iter is reached max 。
(9) And the model updating module is used for updating the model on line, inputting the offline verification data into the training set periodically and updating the least square support vector machine model.
2. The method for realizing the soft measurement of the optimal sea clutter soft measurement instrument based on the fruit fly optimization algorithm optimized least square support vector machine of claim 1, which is characterized in that: the soft measurement method comprises the following steps:
1) Selecting an operation variable and a readily measurable variable as the input of the model for the radar object according to characteristic analysis and climate analysis, wherein the operation variable and the readily measurable variable are obtained from a field database;
2) Preprocessing a model training sample input from a field database, centralizing the training sample, namely subtracting the average value of the sample, and then normalizing the training sample so that the average value is 0 and the variance is 1. The processing is accomplished using the following mathematical process:
2.1 Calculate the mean value:
2.2 Calculate variance:
2.3 Normalization:
wherein TX is a training sample, N is the number of training samples,is the mean of the training samples, X is the normalized training sample, σ x To calculate the variance.
3) And modeling the training sample transmitted from the data preprocessing module by adopting a least square support vector machine. Let the ith normalized training sample X i Target output of O i The least squares support vector machine equates the modeling problem to the following quadratic programming problem by transformation:
where R (w, ξ) is the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ = 1 ,…,ξ N Is the relaxation variable, w is the normal vector of the hyperplane of the least squares support vector machine, b is the corresponding offset, and ω is i I =1, \8230, N and γ are the weight and penalty factors, respectively, of a least squares support vector machine, whereIs the relaxation variable xi i Estimate of standard deviation, constant c 1 ,c 2 Is usually taken as c 1 ＝2.5，c 2 =3, from which a normalized training sample X can be obtained i The output of (c) is:
wherein, K<·> is the kernel function of a least squares support vector machine, where K<·&Taking a linear kernel function; alpha is alpha m M =1, \ 8230, N is the corresponding lagrange multiplier.
4) Optimizing punishment factors and error tolerance values of the least square support vector machine by adopting a drosophila optimization algorithm, and specifically comprising the following steps:
(1) determining optimization parameters of the drosophila optimization algorithm as a penalty factor and an error tolerance value of a least square support vector machine module, the individual number of particle swarm popsize and the maximum cyclic optimization number iter max And initial position regions X _ axis, Y _ axis of the p-th particle.
(2) Setting an optimization objective function, converting the optimization objective function into fitness, calculating the fitness function through a corresponding error function, considering that the fitness of the particles with large errors is small, and expressing the fitness function f of the particles p as:
f p ＝1/(E p +1) (8)
in the formula, E p Is an error function of a least squares support vector machine model, expressed as:
in the formula (I), the compound is shown in the specification,is the predicted output of the least squares support vector machine model, O i Outputting a target of a least squares support vector machine model; n is the number of training samples;
(3) according to the following formula, the particles are searched,
in the formula, randomValue is the search distance;
(4) for the particle p, the distance Dist from the origin is estimated in advance, and the taste concentration determination value S is calculated as the reciprocal distance:
Dist i ＝(X i 2 +Y i 2 ) 1/2 (11)
S i ＝1/Dist i (12)
(5) judging the taste concentration value S i A substitute taste concentration judgment function (or fitness function) for determining the taste concentration Smell of the individual positions of the drosophila i :
[bestSmell bestIndex]＝min(Smell) (13)
(6) Finding the individuals with the best taste concentration in the drosophila population, taking the minimum value:
[bestSmell bestIndex]＝min(Smell) (14)
(7) the optimal individual position and taste concentration values were recorded, at which time all drosophila individuals would fly visually to this position:
(8) judging whether the performance requirements are met, if so, finishing the optimization to obtain a group of optimized parameters of the least square support vector machine; otherwise, returning to the step (3), and continuing the iteration optimization until the maximum iteration number iter is reached max 。
5) And inputting the offline experimental data into a training set regularly, and updating the least square support vector machine model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711117119.7A CN107942304A (en) | 2017-11-13 | 2017-11-13 | Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711117119.7A CN107942304A (en) | 2017-11-13 | 2017-11-13 | Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107942304A true CN107942304A (en) | 2018-04-20 |
Family
ID=61934931
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711117119.7A Pending CN107942304A (en) | 2017-11-13 | 2017-11-13 | Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107942304A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109598083A (en) * | 2018-12-18 | 2019-04-09 | 江南大学 | A kind of glutamic acid fermentation process soft-measuring modeling method based on the twin support vector regression of drosophila algorithm optimization |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080007450A1 (en) * | 2006-07-07 | 2008-01-10 | Jonathan Yedidia | Method and system for determining unwrapped phases from noisy two-dimensional wrapped-phase images |
| CN102147463A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for forecasting Qunzhi radar sea clutters |
| CN102147465A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for detecting sea target by chaos optimizing radar |
| CN102147464A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | Intelligent system and method for forecasting robust radar sea clutter |
| CN103675006A (en) * | 2013-09-22 | 2014-03-26 | 浙江大学 | Least-squares-based industrial melt index soft measuring meter and method |
-
2017
- 2017-11-13 CN CN201711117119.7A patent/CN107942304A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080007450A1 (en) * | 2006-07-07 | 2008-01-10 | Jonathan Yedidia | Method and system for determining unwrapped phases from noisy two-dimensional wrapped-phase images |
| CN102147463A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for forecasting Qunzhi radar sea clutters |
| CN102147465A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for detecting sea target by chaos optimizing radar |
| CN102147464A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | Intelligent system and method for forecasting robust radar sea clutter |
| CN103675006A (en) * | 2013-09-22 | 2014-03-26 | 浙江大学 | Least-squares-based industrial melt index soft measuring meter and method |
Non-Patent Citations (1)
| Title |
|---|
| 王文川: "基于人工智能算法的聚丙烯熔融指数预报建模与优化研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109598083A (en) * | 2018-12-18 | 2019-04-09 | 江南大学 | A kind of glutamic acid fermentation process soft-measuring modeling method based on the twin support vector regression of drosophila algorithm optimization |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107703493A (en) | Sea clutter optimal soft survey instrument and method based on adaptive drosophila optimized algorithm Optimized Least Square Support Vector | |
| CN102147465B (en) | System and method for detecting sea target by chaos optimizing radar | |
| Valavanis et al. | Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS | |
| CN107703491A (en) | Sea clutter optimal soft survey instrument and method based on improved drosophila optimized algorithm optimization RBF neural | |
| Waters et al. | Data assimilation of partitioned HF radar wave data into Wavewatch III | |
| CN108613645B (en) | A kind of Pb-Zn deposits absorbing well, absorption well surveying on sludge thickness method based on parameter Estimation | |
| CN107907872A (en) | Sea clutter optimal soft survey instrument and method based on TSP question drosophila optimization algorithm optimization RBF neural | |
| CN108256546A (en) | Method and system for detecting height of atmospheric boundary layer under non-precipitation condition | |
| Zhou et al. | Time series prediction methods for depth-averaged current velocities of underwater gliders | |
| CN107942312A (en) | A kind of Intelligent radar sea target detection system and method based on differential evolution invasive weed optimization algorithm | |
| CN111366617A (en) | Semi-supervised fuzzy recognition model and soil humidity measurement method based on model | |
| CN108896996B (en) | A kind of Pb-Zn deposits absorbing well, absorption well water sludge interface ultrasonic echo signal classification method based on random forest | |
| CN111505649B (en) | Towed passive array sonar low signal-to-noise ratio ship moving target detection method | |
| CN107656250A (en) | A kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm | |
| CN107942304A (en) | Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine | |
| CN107818224A (en) | Sea clutter optimal soft survey instrument and method based on drosophila optimized algorithm Optimization of Wavelet neutral net | |
| CN108983178A (en) | A kind of Intelligent radar sea target detection system that agility is adaptive | |
| CN108983181A (en) | A kind of radar marine target detection system of gunz optimizing | |
| CN108898220A (en) | Sewage treatment is discharged TP interval prediction method | |
| CN107942313A (en) | Sea clutter optimal soft survey instrument and method based on TSP question drosophila optimization algorithm optimization wavelet neural network | |
| CN115906667B (en) | Ocean environment parameter inversion model construction method and device | |
| CN107843879A (en) | Sea clutter optimal soft survey instrument and method based on free searching algorithm Optimization of Wavelet neutral net | |
| CN107832831A (en) | Sea clutter optimal soft survey instrument and method based on free searching algorithm optimization RBF neural | |
| CN107831482A (en) | Sea clutter optimal soft survey instrument and method based on improved free searching algorithm optimization RBF neural | |
| CN114935759A (en) | Wave field missing value filling method and system based on high-frequency ground wave radar observation |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180420 |
|
| RJ01 | Rejection of invention patent application after publication |