CN107703493A - Sea clutter optimal soft survey instrument and method based on adaptive drosophila optimized algorithm Optimized Least Square Support Vector - Google Patents
Sea clutter optimal soft survey instrument and method based on adaptive drosophila optimized algorithm Optimized Least Square Support Vector Download PDFInfo
- Publication number
- CN107703493A CN107703493A CN201711117137.5A CN201711117137A CN107703493A CN 107703493 A CN107703493 A CN 107703493A CN 201711117137 A CN201711117137 A CN 201711117137A CN 107703493 A CN107703493 A CN 107703493A
- Authority
- CN
- China
- Prior art keywords
- support vector
- vector machine
- optimization
- function
- axism
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
Abstract
A most young waiter in a wineshop or an inn is optimized into the sea clutter optimal soft survey instrument and method of SVMs based on TSP question drosophila the invention discloses a kind of, including radar, field intelligent instrument, control station, the spot database for depositing data, the optimal hard measurement host computer based on improved drosophila optimized algorithm Optimization of Wavelet neutral net and forecast hard measurement value display instrument, 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 SVMs, including data preprocessing module, wavelet neural network module, model modification module based on TSP question drosophila.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 sea clutter optimal soft measuring instrument and method based on a self-adaptive drosophila optimization algorithm optimization 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 surface 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 precondition 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. The wave energy of the sea helps us to study the dynamic characteristics of the sea, but if targets are to be detected from the background of the sea clutter, such as floating ice, ships, etc., it becomes a great 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 Jiang Hai clutter background. The establishment of an accurate sea clutter model is the key to achieving the above objectives
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 effects are 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 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 generally has weak 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 is deduced based on foraging behavior of Fruit flies. In the whole optimizing process of the traditional fruit fly optimizing algorithm, once the optimal individual of the iteration is found, the fruit fly population can be gathered to the individual. If the found optimal fruit fly individual is not the global optimal point, the optimal fruit fly individual is likely to fall into local optimal, the convergence speed and the convergence precision are reduced, and the premature convergence problem is caused. In order to overcome the defects, a self-adaptive variant drosophila optimization algorithm is provided, and the algorithm can ensure that local optima are jumped out, and other spaces are searched continuously until a global optima point is found. The adaptive variant 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 adaptive drosophila optimization algorithm optimization least square support vector machine, which have the advantages of online measurement, high calculation speed, automatic model updating, strong noise resistance and good popularization performance.
The purpose of the invention is realized by the following technical scheme: a sea clutter optimal soft measuring instrument based on a self-adaptive drosophila optimization algorithm optimization 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 forecast soft measurement value display instrument; the field intelligent instrument and the control station are connected with the radar, the field intelligent instrument and the control station are connected with a field database, the soft measuring instrument further comprises an optimal soft measuring upper computer based on an adaptive fruit fly optimization algorithm optimization least square support vector machine, the field database is connected with the input end of the optimal soft measuring upper computer based on the adaptive fruit fly optimization algorithm optimization least square support vector machine, and the output end of the optimal soft measuring upper computer based on the adaptive fruit fly optimization algorithm optimization least square support vector machine 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 adaptive fruit fly 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 for modeling. Model ith normalized training sample X i Target output of (2) is 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, …, N and γ 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 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, …, N is the corresponding lagrange multiplier.
The adaptive fruit fly optimization algorithm module is used for optimizing punishment factors and error tolerance values of the least square support vector machine by adopting an adaptive fruit fly optimization algorithm, and comprises the following specific steps of:
(1) determining optimization parameters of the self-adaptive 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 Variance threshold delta of group fitness 1 Theoretical bestFigure of merit delta 2 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, 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:
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 :
(6) The mean taste of the Drosophila population Smell was calculated according to equation (14) ave Then, the fruit fly population fitness variance tau is calculated according to the formula (15) 2
(7) If τ is 2 ≤δ 1 And Smellbest>δ 2 Or distributed in [0,1]Random number r between&P, then, firstly, the optimal drosophila individuals (X _ axism) are replicated according to the formula (10) j ,Y_axism j ) (j =1,2, …, M); secondly, the copied optimal fruit fly individual is mutated according to the formula (11), and the position of the copied optimal fruit fly individual is updated to be a new position (X _ axism) j ,Y_axism j )(j＝1,2,…, M):
(8) The new position (X _ axism) is estimated again first according to the following equation j ,Y_axism j ) And the distance Dist 'from the original point, and then calculating a new position taste concentration judgment value S' according to a formula:
(9) the taste concentration determination value S 'calculated again is substituted into the taste concentration determination function to calculate the taste concentration Smell' at the new position.
Smell i '＝Function(S i ') (19)
If the R satisfies Smell i '&l < Smellbest > then Smellbest = Smell' j ,X_axis＝X_axism' j , Y_axis＝Y_axism' j ,(j＝1,2,…,M).
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 (5), and continuing the iteration optimization until the maximum iteration number iter is reached max 。
As a preferred scheme, the optimal soft measurement upper computer for optimizing the least square support vector machine based on the adaptive drosophila optimization algorithm 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 an adaptive drosophila optimization algorithm optimization least square support vector machine comprises the following steps:
1) Selecting an operation variable and an easily-measured variable as the input of a model for the radar object according to characteristic analysis and climate 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 sample X after model 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, …, N and γ 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 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, …, N is the corresponding lagrange multiplier.
4) The method adopts a self-adaptive drosophila optimization algorithm to optimize the punishment factor and the error tolerance value of the least square support vector machine, and comprises the following specific steps:
(1) determining optimization parameters of the self-adaptive 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 Variance threshold delta of group fitness 1 Theoretical optimum value delta 2 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, 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 For least-squares support vector machine modelsOutputting a target; 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 :
(6) The mean taste of the Drosophila population Smell was calculated according to equation (14) ave Then calculating the fruit fly population fitness variance tau according to the formula (15) 2
(7) If τ is 2 ≤δ 1 And Smellbest>δ 2 Or distributed in [0,1]Random number r between&P, then, firstly, the optimal fruit fly individual (X _ axism) is duplicated according to the formula (10) j ,Y_axism j ) (j =1,2, …, M); according to the formula (11 Carrying out mutation on the copied optimal fruit fly individual, and updating the position of the copied optimal fruit fly individual to a new position (X _ axism) j ,Y_axism j )(j＝1,2,…, M):
(8) The new position (X _ axism) is estimated again first according to the following equation j ,Y_axism j ) And the distance Dist 'from the original point, and then calculating a new position taste concentration judgment value S' according to a formula:
(9) the taste concentration determination value S 'calculated again is substituted into the taste concentration determination function to calculate the taste concentration Smell' at the new position. Smell i '＝Function(S i ')(19)
If the R satisfies Smell i '&l < Smellbest > then Smellbest = Smell' j ,X_axis＝X_axism' j , Y_axis＝Y_axism' j ,(j＝1,2,…,M).
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 (5), 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: 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 method has the advantages that online optimal soft measurement is carried out on the sea clutter, the defects that an existing sea clutter measuring instrument is low in measuring precision, low in noise sensitivity and poor in popularization performance are overcome, the adaptive drosophila optimization algorithm is introduced to carry out automatic optimization on a least square support vector machine model, and parameters of the least square support vector machine are adjusted for multiple times without human experience, so that an 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 model prediction output and larger prediction errors can be caused, and the model automatically optimizes the parameters of the model through a self-adaptive variation drosophila optimization algorithm to obtain an optimal soft measurement model.
The invention has the following beneficial effects: the online soft measurement of the sea clutter is realized, and the online soft measurement has the advantages of strong noise resistance, high interference resistance, high precision and strong popularization capability.
Drawings
FIG. 1 is a schematic diagram of the basic structure of an optimal soft measurement instrument and method for a sea clutter modeling process based on an adaptive drosophila optimization algorithm optimization least square support vector machine;
FIG. 2 is a schematic diagram of an optimal soft measurement upper computer structure based on an adaptive drosophila optimization algorithm optimization least square support vector machine.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The examples are intended to illustrate the invention, but not to limit the invention, and any modifications and variations of the invention within the spirit and scope of the claims are intended to fall within the scope of the invention.
Example 1
Referring to fig. 1 and 2, the sea clutter optimal soft measuring instrument based on the adaptive drosophila optimization algorithm optimization least square support vector machine comprises a radar 1, a field intelligent instrument 2 used for measuring easily-measured variables, a control station 3 used for measuring operation variables, a field database 4 used for storing data and a sea clutter soft measurement value display instrument 6, wherein the field intelligent instrument 2 and the control station 3 are connected with the radar 1, the field intelligent instrument 2 and the control station 3 are connected with the field database 4, the soft measuring instrument further comprises an optimal soft measurement upper computer 5 of the adaptive drosophila optimization algorithm optimization least square support vector machine, the field database 4 is connected with the input end of the optimal soft measurement upper computer 5 based on the adaptive drosophila optimization algorithm optimization support vector machine, and the output end of the optimal soft measurement upper computer 5 of the adaptive drosophila 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 measurable variable of the radar object and transmits the easily measurable 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 adaptive fruit fly optimization algorithm optimization least square support vector machine, and the sea clutter 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 adaptive fruit fly optimization algorithm optimization least square support vector machine. The optimal soft measurement upper computer 5 based on the least square support vector machine optimized by the adaptive fruit fly 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, 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, …, N and γ are the weight and penalty factors, respectively, of the least squares support vector machine, whereIs the relaxation variable xi i The estimation of the standard deviation is carried out,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, …, N is the corresponding lagrange multiplier.
The adaptive fruit fly optimization algorithm module 9 is used for optimizing the punishment factor and the error tolerance value of the least square support vector machine by adopting an adaptive fruit fly optimization algorithm, and comprises the following specific steps:
(1) determining optimization parameters of the self-adaptive 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 Variance threshold delta of group fitness 1 Theoretical optimum value delta 2 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, 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 For least squares support vector machine modelA target output of the 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 :
(6) The mean taste of the Drosophila population Smell was calculated according to equation (14) ave Then, the fruit fly population fitness variance tau is calculated according to the formula (15) 2
(7) If τ is 2 ≤δ 1 And Smellbest>δ 2 Or distributed in [0,1]Random number r between&P, then, firstly, the optimal fruit fly individual (X _ axism) is duplicated according to the formula (10) j ,Y_axism j ) (j =1,2, …, M); followed byThe copy optimal fruit fly individual is mutated in the formula (11), and the position of the copy optimal fruit fly individual is updated to a new position (X _ axism) j ,Y_axism j )(j＝1,2,…, M):
(8) The new position (X _ axism) is estimated again first according to the following equation j ,Y_axism j ) And the distance Dist 'from the original point, and then calculating a new position taste concentration judgment value S' according to a formula:
(9) the taste concentration determination value S 'calculated again is substituted into the taste concentration determination function to calculate the taste concentration Smell' at the new position.
Smell i '＝Function(S i ') (19)
If the R satisfies Smell i '&l < Smellbest > then Smellbest = Smell' j ,X_axis＝X_axism' j , Y_axis＝Y_axism' j ,(j＝1,2,…,M).
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 (5), and continuing the iteration optimization until the maximum iteration number iter is reached max 。
As a preferred scheme, the optimal soft measurement upper computer for optimizing the least square support vector machine based on the adaptive 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 sea clutter optimal soft measurement method based on an adaptive 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 sample X after model 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, …, N and γ 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 usually 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 (alpha) ("alpha") m M =1, …, N is the corresponding lagrange multiplier.
4) The method adopts a self-adaptive drosophila optimization algorithm to optimize the punishment factor and the error tolerance value of the least square support vector machine, and comprises the following specific steps:
(1) determining an adaptive fruit fly optimization algorithmThe optimization parameters are a penalty factor and an error tolerance value of a least square support vector machine module, the individual number of particle swarms popsize and the maximum cyclic optimization number iter max Variance threshold delta of group fitness 1 Theoretical optimum value delta 2 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, 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 Concentration of taste of the personA determination function (or fitness function) for determining the taste concentration Smell of the individual positions of the fruit flies i :
(6) The mean taste of the Drosophila population Smell was calculated according to equation (14) ave Then, the fruit fly population fitness variance tau is calculated according to the formula (15) 2
(7) If τ is 2 ≤δ 1 And Smellbest>δ 2 Or distributed in [0,1]Random number r between&P, then, firstly, the optimal fruit fly individual (X _ axism) is duplicated according to the formula (10) j ,Y_axism j ) (j =1,2, …, M); secondly, the copied optimal fruit fly individual is mutated according to the formula (11), and the position of the copied optimal fruit fly individual is updated to be a new position (X _ axism) j ,Y_axism j )(j＝1,2,…, M):
(8) The new position (X _ axism) is estimated again first according to the following equation j ,Y_axism j ) And the distance Dist 'from the original point, and then calculating a new position taste concentration judgment value S' according to a formula:
(9) the taste concentration determination value S 'calculated again is substituted into the taste concentration determination function to calculate the taste concentration Smell' at the new position.
Smell i '＝Function(S i ') (19)
If the R satisfies Smell i '&l < Smellbest > then Smellbest = Smell' j ,X_axis＝X_axism' j ,
Y_axis＝Y_axism' j ,(j＝1,2,…,M).
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 (5), 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 variables and the easily measurable variables 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.
Step 2: and sample data is preprocessed and completed by a data preprocessing module 7.
And step 3: and establishing an initial least square support vector machine model 8 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 adaptive drosophila optimization algorithm module 9.
And 5: the model updating module 10 periodically inputs offline experimental 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 adaptive drosophila optimization algorithm to complete establishment.
And 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 self-adaptive drosophila optimization algorithm optimized least square support vector machine model, and the optimal soft measurement display of the sea clutter is completed.
The optimal soft measurement upper computer based on the adaptive variation fruit fly 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 the 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 sea clutter optimal soft measuring instrument based on a self-adaptive drosophila optimization algorithm optimization 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 measurement 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 an adaptive fruit fly optimization algorithm optimization least square support vector machine, the field database is connected with the input end of the optimal soft measuring upper computer based on the adaptive fruit fly optimization algorithm optimization least square support vector machine, and the output end of the optimal soft measuring upper computer based on the adaptive fruit fly optimization algorithm optimization least square support vector machine 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 adaptive fruit fly 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 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, 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, …, N and γ 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 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, …, N is the corresponding lagrange multiplier.
The self-adaptive fruit fly optimization algorithm module is used for optimizing a punishment factor and an error tolerance value of a least square support vector machine by adopting a self-adaptive fruit fly optimization algorithm, and comprises the following specific steps of:
(1) determining optimization parameters of the self-adaptive 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 Variance threshold delta of group fitness 1 Theoretical optimum value delta 2 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, E p Is a least squares supportError function of the 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 :
(6) The mean taste of the Drosophila population Smell was calculated according to equation (14) ave Then, the fruit fly population fitness variance tau is calculated according to the formula (15) 2
(7) If τ is 2 ≤δ 1 And Smellbest>δ 2 Or distributed in [0,1]Random number r between&P, then, firstly, the optimal fruit fly individual (X _ axism) is duplicated according to the formula (10) j ,Y_axism j ) (j =1,2, …, M); secondly, the copied optimal fruit fly individual is mutated according to the formula (11), and the position of the copied optimal fruit fly individual is updated to be a new position (X _ axism) j ,Y_axism j )(j＝1,2,…,M):
(8) The new position (X _ axism) is estimated again first according to the following equation j ,Y_axism j ) And the distance Dist 'from the original point, and then calculating a new position taste concentration judgment value S' according to a formula:
(9) the taste concentration determination value S 'calculated again is substituted into the taste concentration determination function to calculate the taste concentration Smell' at the new position.
Smell i '＝Function(S i ') (19)
If the R satisfies Smell i '&l < Smellbest > then Smellbest = Smell' j ,X_axis＝X_axism' j ,Y_axis＝Y_axism' j ,(j＝1,2,…,M).
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 (5), and continuing to iterate and optimize until reaching the maximum iteration number iter max 。
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 sea clutter optimal soft measurement instrument based on the adaptive drosophila optimization algorithm optimized least square support vector machine according to the claim 1, is characterized in that: the soft measurement method comprises the following steps:
1) Selecting an operation variable and an easily-measured variable as the input of a model for the radar object according to characteristic analysis and climate 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 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, X is the normalized training sample, σ x To calculate the variance.
3) For pre-processing slave dataAnd modeling the training sample transmitted by the processing 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:
wherein 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, …, N and γ 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 usually 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, …, N is the corresponding lagrange multiplier.
4) Optimizing the punishment factor and the error tolerance value of the least square support vector machine by adopting a self-adaptive drosophila optimization algorithm, and specifically comprising the following steps of:
(1) determining optimization parameters of the self-adaptive 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 Variance threshold delta of group fitness 1 Theoretical optimum value delta 2 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, 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,
wherein, random Value 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 :
(6) The mean taste of the Drosophila population Smell was calculated according to equation (14) ave Then calculating the fruit fly population fitness variance tau according to the formula (15) 2
(7) If τ is 2 ≤δ 1 And Smellbest>δ 2 Or distributed in [0,1]Random number r between&P, then, firstly, the optimal fruit fly individual (X _ axism) is duplicated according to the formula (10) j ,Y_axism j ) (j =1,2, …, M); secondly, the copied optimal fruit fly individual is mutated according to the formula (11), and the position of the copied optimal fruit fly individual is updated to be a new position (X _ axism) j ,Y_axism j )(j＝1,2,…,M):
(8) The new position (X _ axism) is estimated again first according to the following equation j ,Y_axism j ) And the distance Dist 'from the original point, and then calculating a new position taste concentration judgment value S' according to a formula:
(9) the taste concentration determination value S 'calculated again is substituted into the taste concentration determination function to calculate the taste concentration Smell' at the new position.
Smell i '＝Function(S i ') (19)
If R satisfies Smell i '&l < Smellbest > then Smellbest = Smell' j ,X_axis＝X_axism' j ,Y_axis＝Y_axism' j ,(j＝1,2,…,M).
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 (5), 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 |
|---|---|---|---|
| CN201711117137.5A CN107703493A (en) | 2017-11-13 | 2017-11-13 | Sea clutter optimal soft survey instrument and method based on adaptive drosophila optimized algorithm Optimized Least Square Support Vector |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711117137.5A CN107703493A (en) | 2017-11-13 | 2017-11-13 | Sea clutter optimal soft survey instrument and method based on adaptive drosophila optimized algorithm Optimized Least Square Support Vector |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107703493A true CN107703493A (en) | 2018-02-16 |
Family
ID=61178502
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711117137.5A Pending CN107703493A (en) | 2017-11-13 | 2017-11-13 | Sea clutter optimal soft survey instrument and method based on adaptive drosophila optimized algorithm Optimized Least Square Support Vector |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107703493A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108536955A (en) * | 2018-04-08 | 2018-09-14 | 西安邮电大学 | A kind of hybrid forecasting method of underwater sound signal |
| CN108681249A (en) * | 2018-05-14 | 2018-10-19 | 浙江大学 | A kind of probabilistic type that parameter independently optimizes output fault diagnosis system |
| CN108681250A (en) * | 2018-05-14 | 2018-10-19 | 浙江大学 | A kind of improvement machine learning fault diagnosis system based on colony intelligence optimization |
| CN108681248A (en) * | 2018-05-14 | 2018-10-19 | 浙江大学 | A kind of autonomous learning fault diagnosis system that parameter is optimal |
| CN109002918A (en) * | 2018-07-16 | 2018-12-14 | 国网浙江省电力有限公司经济技术研究院 | Based on drosophila optimization algorithm-support vector machines electricity sales amount prediction technique |
| 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 |
| CN110031847A (en) * | 2018-09-29 | 2019-07-19 | 浙江师范大学 | The dynamic of wavelet transformation and support vector machines combination radar reflectivity is short to face Quantitative Precipitation estimating and measuring method |
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 |
| CN102147464A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | Intelligent system and method for forecasting robust radar sea clutter |
| CN102147465A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for detecting sea target by chaos optimizing radar |
| 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 CN201711117137.5A patent/CN107703493A/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 |
| CN102147464A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | Intelligent system and method for forecasting robust radar sea clutter |
| CN102147465A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for detecting sea target by chaos optimizing radar |
| 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 (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108536955A (en) * | 2018-04-08 | 2018-09-14 | 西安邮电大学 | A kind of hybrid forecasting method of underwater sound signal |
| CN108681249A (en) * | 2018-05-14 | 2018-10-19 | 浙江大学 | A kind of probabilistic type that parameter independently optimizes output fault diagnosis system |
| CN108681250A (en) * | 2018-05-14 | 2018-10-19 | 浙江大学 | A kind of improvement machine learning fault diagnosis system based on colony intelligence optimization |
| CN108681248A (en) * | 2018-05-14 | 2018-10-19 | 浙江大学 | A kind of autonomous learning fault diagnosis system that parameter is optimal |
| CN109002918A (en) * | 2018-07-16 | 2018-12-14 | 国网浙江省电力有限公司经济技术研究院 | Based on drosophila optimization algorithm-support vector machines electricity sales amount prediction technique |
| CN110031847A (en) * | 2018-09-29 | 2019-07-19 | 浙江师范大学 | The dynamic of wavelet transformation and support vector machines combination radar reflectivity is short to face Quantitative Precipitation estimating and measuring method |
| CN110031847B (en) * | 2018-09-29 | 2022-08-23 | 浙江师范大学 | Dynamic short-term quantitative rainfall estimation method combining wavelet transformation and support vector machine with radar reflectivity |
| 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 | |
| Valavanis et al. | Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS | |
| CN112001270B (en) | Ground radar automatic target classification and identification method based on one-dimensional convolutional neural network | |
| CN110689179A (en) | Water bloom prediction method based on space-time sequence mixed model | |
| 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 | |
| Zhao et al. | XGB model: Research on evaporation duct height prediction based on XGBoost algorithm | |
| Dorn | Fishing behavior of factory trawlers: a hierarchical model of information processing and decision-making | |
| CN108983179A (en) | A kind of radar marine target detection system of colony intelligence agility | |
| CN110501683A (en) | A kind of extra large land Clutter Classification method based on 4 D data feature | |
| Sattari et al. | Estimation of monthly and seasonal precipitation: A comparative study using data-driven methods versus hybrid approach | |
| CN108896996A (en) | A kind of Pb-Zn deposits absorbing well, absorption well water sludge interface ultrasonic echo signal classification method based on random forest | |
| CN115906667B (en) | Ocean environment parameter inversion model construction method and device | |
| 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 | |
| CN107942304A (en) | Sea clutter optimal soft survey instrument and method based on drosophila optimization algorithm optimization least square method supporting vector machine | |
| CN107942313A (en) | Sea clutter optimal soft survey instrument and method based on TSP question drosophila optimization algorithm optimization wavelet neural network | |
| Liu et al. | Prediction of the Antarctic marginal ICE zone extent based upon its multifractal property | |
| 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 | |
| Mathew et al. | Inverse estimation of wind from the waves measured by high-frequency radar | |
| De et al. | Preference of echo features for classification of seafloor sediments using neural networks |
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: 20180216 |
|
| RJ01 | Rejection of invention patent application after publication |