CN107832831A - Sea clutter optimal soft survey instrument and method based on free searching algorithm optimization RBF neural - Google Patents
Sea clutter optimal soft survey instrument and method based on free searching algorithm optimization RBF neural Download PDFInfo
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Abstract
The invention discloses a kind of sea clutter optimal soft survey instrument and method based on free searching algorithm optimization RBF neural, including radar, field intelligent instrument, control station, the spot database for depositing data, optimal hard measurement host computer and forecast hard measurement value display instrument based on improved drosophila optimized algorithm optimization RBF neural；The described optimal hard measurement host computer based on free searching algorithm optimization RBF neural, including data preprocessing module, RBF neural module, model modification module.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 present invention relates to optimal soft survey instrument and method field, is specifically that one kind optimizes RBF based on free searching algorithm
The sea clutter optimal soft survey instrument and method of neutral net.
Background technology
The echo-signal reflected from seawater surface is called sea clutter, sea clutter and sea condition, agitation, radar in field of radar
The many factors such as parameter are relevant.Worked in for seaward detector, shipborne radar etc. for the radar of marine environment, serious
Sea surface reflection echo will influence the detection to sea-surface target and tracking performance, grasp the property of sea clutter, and it is miscellaneous to establish accurately sea
Wave pattern is analysis and the premise for improving radar performance.The statistical property of sea clutter includes amplitude characteristic and correlation properties.Sea is miscellaneous
The correlation properties of ripple include temporal correlation and spatial coherence.Temporal correlation is also referred to as pulse-to-pulse correlation, and it is miscellaneous to reflect sea
Wave amplitude can be represented equivalently with the fluctuating of time with power spectrum.The spatial coherence of sea clutter is divided into attitude corrections
And distance correlation.The dynamic characteristic that sea clutter can study ocean for us provides help, still, if will be from sea clutter background
Lower detection target, such as floating ice, ship, it just turns into very big obstacle, it is necessary to suppresses as much as possible to weaken or eliminate these
Interference.Study the main purpose of sea clutter：On the one hand it is that the natural mechanism of sea clutter is explained, and then proposes rational mould
Type；On the other hand, it is interference of the sea clutter to be reduced to detection target, finds out and how will drown out the target in strong sea clutter background
The method that signal extraction comes out.The foundation of accurate sea clutter model is to realize the key of above-mentioned each purpose
The research work on the Modeling and Prediction of sea clutter is largely all concentrated on above artificial neural network in recent years, is taken
Obtained good effect.But artificial neural network the shortcomings that also having its own, such as the interstitial content of over-fitting, hidden layer and
The bad determination of parameter.Secondly, the data that observation site collects are not also because noise, manual operation error etc. be with necessarily true
Error is determined, so not strong using the general Generalization Ability of forecasting model of the strong artificial neural network of certainty.SVMs, by
Vapnik introduced in 1998, due to its good Generalization Ability, was widely used in pattern-recognition, fitting and classification problem
In.Because standard SVMs is sensitive to isolated point and noise, so having also been proposed RBF neural later.RBF nerve nets
Network can preferably handle the sample data with noise compared to standard SVMs, be used for modeling here.Freely search for
Algorithm, i.e. Fruit Fly Optimization Algorithm, by TaiWan, China, Wen-Tsao professors Pan put forward
A kind of a kind of biological intelligence optimizing algorithm deduced out based on drosophila foraging behavior from you, abbreviation FOA.The algorithm passes through in colony
It is interparticle to influence each other, reduce the risk that searching algorithm is absorbed in locally optimal solution, there is good global search performance.From
The best parameter group of RBF neutral nets is used to search for by searching algorithm, to reach the purpose of Optimized model.
The content of the invention
In order to overcome the shortcomings of the measurement accuracy of existing radar it is not high, it is low to noise sensitivity, promote poor performance, this hair
It is bright a kind of on-line measurement is provided, calculating speed is fast, model automatically updates, noise resisting ability is strong, it is good based on freedom to promote performance
Searching algorithm optimizes the sea clutter optimal soft survey instrument and method of RBF neural.
The purpose of the present invention is achieved through the following technical solutions：One kind is based on free searching algorithm optimization RBF god
Sea clutter optimal soft survey instrument through network, including radar, for measure easily survey variable field intelligent instrument, for measuring
The control station of performance variable, the spot database for depositing data and sea clutter forecast hard measurement value display instrument, the live intelligence
Energy instrument, control station and propylene polymerization production process connect, and the field intelligent instrument, control station and spot database connect,
The soft measuring instrument also includes the optimal hard measurement host computer based on free searching algorithm optimization RBF neural, described existing
Field data storehouse is connected with the input of the optimal hard measurement host computer based on free searching algorithm optimization RBF neural,
The output end and sea clutter hard measurement value of the optimal hard measurement host computer based on free searching algorithm optimization RBF neural
Display instrument connects, and the optimal hard measurement host computer based on free searching algorithm optimization RBF neural includes：
Data preprocessing module, for the model training inputted from spot database sample to be pre-processed, to training
Center of a sample, that is, the average value of sample is subtracted, then it is standardized：
Calculate average：
Calculate variance：
Standardization：
Wherein, TX is training sample, and N is number of training,For the average of training sample, X is the training after standardization
Sample, σxTo calculate variance.
" base " of the RBF neural by the use of RBF as hidden unit forms implicit sheaf space, and defeated people's vector is direct
It is linear to be mapped to mapping of the implicit sheaf spaces of implicit sheaf space to output space.In general, network is defeated by being input to
The mapping gone out is nonlinear, and network output for adjustable parameter but is linear.Theory is it has been proved that RBF networks
It is that the optimal network of mapping function is completed in feedforward neural network with global and optimal approximation capability.As long as have enough
Hidden neuron, RBF can be with the approximate any continuous functions of arbitrary accuracy.Using feedforward network topological structure, hidden layer it is each
Unit exports:
hi=Ri(X)=Ri(||X-C||/σi) (4)
I=1,2,3 ..., L
Wherein, X is N-dimensional input vector, CiIt is the vector with X with dimension, RiFor Radial basis kernel function.
Ri() generally use Gaussian function, wherein σ are kernel function width, i.e.,：
Therefore the output of network output layer is
Wherein w is connection weight, and h is the unit output of hidden layer.
Free searching algorithm optimization module, for being entered using free searching algorithm to the kernel function width of RBF neural
Row optimization, is comprised the following steps that：
Free search algorithm module, it is excellent for being carried out using free searching algorithm to the kernel function width of RBF neural
Change, comprise the following steps that：
(1) the scale n and dimension m, [X of population are initializedmini, Xmaxi] excursion as i-th dimension, greatest iteration time
Number is Maxgen, and exploration step number is T, and the variable neighborhood value of i-th of body jth dimension is Rji∈[Rmin,Rmax].It is according to these settings
Initialize installation can be carried out to population position：
xtji=x0ji-Δxtji+2Δxtji*randomtji(0,1) (7)
X0jiIt is the initial value randomly generated：
Δxtji=Rji*(Xmax-Xmin)*randomtji(0,1) (8)
(2) each individual is gone into action, and individual i carries out T step detections every time, and the renewal of position is determined by following formula：
x0ji=Xmini+(Xmaxi-Xmini)*randomji(0,1) (9)
In exploration behavior once, individual exploration behavior is represented by：
F (x in formulatji) it is the position that each individual is completed to mark after search.
Pheromones are defined as follows：
And sensitivity updates according to equation below：
S in formulaminAnd SmaxIt is the minimum value and maximum of sensitivity respectively, the minimum value and maximum of pheromones are respectively
PminAnd Pmax, and P is provided hereinmin=SminAnd Pmin=Smax。
After the search of epicycle terminates, according to the starting point of equation below renewal next round search：
(3) the termination strategy of algorithm has 3 kinds of situations：
A) object function has reached its globally optimal solution, i.e. fmax≥fopt；
B) current iteration number g alreadys exceed greatest iteration number Maxgen；
C) any one above-mentioned situation is met.Otherwise repeat step (2).
As a kind of preferable scheme, on the optimal hard measurement based on free searching algorithm optimization RBF neural
Position machine also includes：Model modification module, for the online updating of model, periodically offline analysis data is input in training set,
Update RBF neural network models.
A kind of optimal flexible measurement method of sea clutter based on free searching algorithm optimization RBF neural, the hard measurement
Method comprises the following steps：
1), to radar object, according to specificity analysis and climatic analysis, selection operation variable and easily survey variable are as model
Input, performance variable and easily survey variable are obtained by spot database；
2), the model training sample inputted from spot database is pre-processed, to training sample centralization, that is, subtracted
The average value of sample, is then standardized to it so that its average is 0, variance 1.The processing uses following formula process
To complete：
2.1) average is calculated：
2.2) variance is calculated：
2.3) standardize：
Wherein, TX is training sample, and N is number of training,For the average of training sample, X is the training after standardization
Sample.3), to the training sample for being transmitted through coming from data preprocessing module, it is modeled using RBF neural.RBF nerve nets
" base " of the network by the use of RBF as hidden unit forms implicit sheaf space, and defeated people's vector is mapped directly into implicit sheaf space
The mapping in implicit sheaf space to output space is linear.In general, network is non-linear by the mapping for being input to output
, and network output for adjustable parameter but is linear.Theory is it has been proved that RBF networks have global and most preferably forced
Nearly performance, it is that the optimal network of mapping function is completed in feedforward neural network.As long as there are enough hidden neurons, RBF energy
With the approximate any continuous function of arbitrary accuracy.Using feedforward network topological structure, each unit output of hidden layer is:
hi=Ri(X)=Ri(||X-C||/σi) (4)
I=1,2,3 ..., L
Wherein, X is N-dimensional input vector, CiIt is the vector with X with dimension, RiFor Radial basis kernel function.
Ri() generally use Gaussian function, wherein σ are kernel function width, i.e.,：
Therefore the output of network output layer is
Wherein w is connection weight, and h is the unit output of hidden layer.
4), the penalty factor and error margin value of RBF neural are optimized using free searching algorithm, specific step
It is rapid as follows：
(1) the scale n and dimension m, [X of population are initializedmini, Xmaxi] excursion as i-th dimension, greatest iteration time
Number is Maxgen, and exploration step number is T, and the variable neighborhood value of i-th of body jth dimension is Rji∈[Rmin,Rmax].It is according to these settings
Initialize installation can be carried out to population position：
xtji=x0ji-Δxtji+2Δxtji*randomtji(0,1) (7)
X0jiIt is the initial value randomly generated：
Δxtji=Rji*(Xmax-Xmin)*randomtji(0,1) (8)
(2) each individual is gone into action, and individual i carries out T step detections every time, and the renewal of position is determined by following formula：
x0ji=Xmini+(Xmaxi-Xmini)*randomji(0,1) (9)
In exploration behavior once, individual exploration behavior is represented by：
F (x in formulatji) it is the position that each individual is completed to mark after search.
Pheromones are defined as follows：
And sensitivity updates according to equation below：
S in formulaminAnd SmaxIt is the minimum value and maximum of sensitivity respectively, the minimum value and maximum of pheromones are respectively
PminAnd Pmax, and P is provided hereinmin=SminAnd Pmin=Smax。
After the search of epicycle terminates, according to the starting point of equation below renewal next round search：
(3) the termination strategy of algorithm has 3 kinds of situations：
A) object function has reached its globally optimal solution, i.e. fmax≥fopt；
B) current iteration number g alreadys exceed greatest iteration number Maxgen；
C) any one above-mentioned situation is met.Otherwise repeat step (2).
As a kind of preferable scheme：The flexible measurement method is further comprising the steps of：5), periodically by offline analysis data
It is input in training set, updates RBF neural network model.
The present invention technical concept be：Online optimal hard measurement is carried out to sea clutter, overcomes existing sea clutter measuring instrument
Table stability is poor, is easily absorbed in the deficiency of local optimum, and it is automatic to RBF neural network model progress excellent to introduce free searching algorithm
Change, it is not necessary to which artificial experience repeatedly to adjust the parameter of RBF neural, to obtain optimal hard measurement result.This model phase
Had the advantage that for existing sea clutter soft-sensing model：It is modeled, is had higher pre- by RBF neural network model
Report precision；Existing model parameter typically determined by the experience of operative employee, have it is certain restricted and uncertain, if
Once value is improper, concussion and bigger prediction error that model prediction exports can be caused, and model passes through free search
Algorithm carries out automatic optimal to the parameter of model, obtains optimal soft-sensing model.
Beneficial effects of the present invention are mainly manifested in：Realize the online optimal hard measurement of sea clutter, overcome it is artificial because
Randomness caused by element influences, and improves the stability of model prediction, reduces the possibility that model prediction is absorbed in local optimum.
Brief description of the drawings
Fig. 1 is the topology diagram of RBF neural；
Fig. 2 be based on free searching algorithm optimization RBF neural sea clutter modeling process optimal soft survey instrument and
The basic structure schematic diagram of method；
Fig. 3 is the optimal hard measurement host computer structural representation based on free searching algorithm optimization RBF neural.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.The embodiment of the present invention is used for illustrating the present invention, without
It is to limit the invention, in the protection domain of spirit and claims of the present invention, any is repaiied to what the present invention made
Change and change, both fall within protection scope of the present invention.
Embodiment 1
Reference picture 1, Fig. 2 and Fig. 3, a kind of optimal soft survey of sea clutter based on free searching algorithm optimization RBF neural
Measure instrument, including radar 1, for measuring easily survey the field intelligent instrument 2 of variable, the control station 3 for measuring performance variable, deposit
Put the spot database 4 and sea clutter hard measurement value display instrument 6 of data, the field intelligent instrument 2, control station 3 and radar 1
Connection, the field intelligent instrument 2, control station 3 are connected with spot database 4, and the soft measuring instrument also includes freely searching for
The optimal hard measurement host computer 5 of algorithm optimization RBF neural, the spot database 4 are based on free searching algorithm with described
Optimize the input connection of the optimal hard measurement host computer 5 of RBF neural, it is described based on free searching algorithm optimization RBF god
The output end of optimal hard measurement host computer 5 through network is connected with sea clutter hard measurement value display instrument 6.Field intelligent instrument 2 is surveyed
The easy survey variable of radar object is measured, easy variable of surveying is transferred to spot database 4；The operation of the control radar object of control station 3 becomes
Amount, spot database 4 is transferred to by performance variable.The variable data recorded in spot database 4, which is used as, to be based on freely searching for calculation
The input of the optimal hard measurement host computer 5 of method optimization RBF neural, hard measurement value display instrument 6, which is used to show, to be based on freely searching
The output of the optimal hard measurement host computer 5 of rope algorithm optimization RBF neural, i.e. hard measurement value.It is described to be based on freely searching for calculation
Method optimizes the optimal hard measurement host computer 5 of RBF neural, including following 4 parts：
Data preprocessing module 7, for the model training inputted from spot database sample to be pre-processed, to training
Center of a sample, that is, the average value of sample is subtracted, then it is standardized：
Calculate average：
Calculate variance：
Standardization：
Wherein, TX is training sample, and N is number of training,For the average of training sample, X is the training after standardization
Sample, σxFor variance.
" base " of the RBF neural by the use of RBF as hidden unit forms implicit sheaf space, and defeated people's vector is direct
It is linear to be mapped to mapping of the implicit sheaf spaces of implicit sheaf space to output space.In general, network is defeated by being input to
The mapping gone out is nonlinear, and network output for adjustable parameter but is linear.Theory is it has been proved that RBF networks
It is that the optimal network of mapping function is completed in feedforward neural network with global and optimal approximation capability.As long as have enough
Hidden neuron, RBF can be with the approximate any continuous functions of arbitrary accuracy.Using feedforward network topological structure, hidden layer it is each
Unit exports:
hi=Ri(X)=Ri(||X-C||/σi) (4)
I=1,2,3 ..., L
Wherein, X is N-dimensional input vector, CiIt is the vector with X with dimension, RiFor Radial basis kernel function.
Ri() generally use Gaussian function, wherein σ are kernel function width, i.e.,：
Therefore the output of network output layer is
Wherein w is connection weight, and h is the unit output of hidden layer.
Free search algorithm module 9, for being held using free searching algorithm to the penalty factor and error of RBF neural
Limit value optimizes, and comprises the following steps that：
(1) the scale n and dimension m, [X of population are initializedmini, Xmaxi] excursion as i-th dimension, greatest iteration time
Number is Maxgen, and exploration step number is T, and the variable neighborhood value of i-th of body jth dimension is Rji∈[Rmin,Rmax].It is according to these settings
Initialize installation can be carried out to population position：
xtji=x0ji-Δxtji+2Δxtji*randomtji(0,1) (7)
X0jiIt is the initial value randomly generated：
Δxtji=Rji*(Xmax-Xmin)*randomtji(0,1) (8)
(2) each individual is gone into action, and individual i carries out T step detections every time, and the renewal of position is determined by following formula：
x0ji=Xmini+(Xmaxi-Xmini)*randomji(0,1) (9)
In exploration behavior once, individual exploration behavior is represented by：
F (x in formulatji) it is the position that each individual is completed to mark after search.
Pheromones are defined as follows：
And sensitivity updates according to equation below：
S in formulaminAnd SmaxIt is the minimum value and maximum of sensitivity respectively, the minimum value and maximum of pheromones are respectively
PminAnd Pmax, and P is provided hereinmin=SminAnd Pmin=Smax。
After the search of epicycle terminates, according to the starting point of equation below renewal next round search：
(3) the termination strategy of algorithm has 3 kinds of situations：
A) object function has reached its globally optimal solution, i.e. fmax≥fopt；
B) current iteration number g alreadys exceed greatest iteration number Maxgen；
C) any one above-mentioned situation is met.Otherwise repeat step (2).
As a kind of preferable scheme, on the optimal hard measurement based on free searching algorithm optimization RBF neural
Position machine also includes：Model modification module 10, for the online updating of model, offline analysis data is periodically input to training set
In, update RBF neural network model.
Embodiment 2
Reference picture 1, Fig. 2 and Fig. 3, a kind of optimal soft survey of sea clutter based on free searching algorithm optimization RBF neural
Amount method, the flexible measurement method comprise the following steps：
1), to radar object, according to industrial analysis and Operations Analyst, selection operation variable and easily survey variable are as model
Input, performance variable and easily survey variable are obtained by spot database；
2), the model training sample inputted from spot database is pre-processed, to training sample centralization, that is, subtracted
The average value of sample, is then standardized to it so that its average is 0, variance 1.The processing uses following formula process
To complete：
2.1) average is calculated：
2.2) variance is calculated：
2.3) standardize：
Wherein, TX is training sample, and N is number of training,For the average of training sample, X is the training sample after standardization
This, σxTo calculate variance.
3), to the training sample for being transmitted through coming from data preprocessing module, it is modeled using RBF neural.RBF nerves
" base " of the network by the use of RBF as hidden unit forms implicit sheaf space, and defeated people's vector is mapped directly into hidden layer sky
Between imply sheaf space to export space mapping be linear.In general, network is non-thread by the mapping for being input to output
Property, and network output for adjustable parameter but is linear.It is theoretical it has been proved that RBF networks have it is global and optimal
Approximation capability, it is that the optimal network of mapping function is completed in feedforward neural network.As long as there are enough hidden neurons, RBF
Can be with the approximate any continuous function of arbitrary accuracy.Using feedforward network topological structure, each unit output of hidden layer is:
hi=Ri(X)=Ri(||X-C||/σi) (4)
I=1,2,3 ..., L
Wherein, X is N-dimensional input vector, CiIt is the vector with X with dimension, RiFor Radial basis kernel function.
Ri(i) generally use Gaussian function, wherein σ are kernel function width, i.e.,：
Therefore the output of network output layer is
Wherein w is connection weight, and h is the unit output of hidden layer.
4), the kernel function width of RBF neural is optimized using free searching algorithm, comprised the following steps that：
(1) the scale n and dimension m, [X of population are initializedmini, Xmaxi] excursion as i-th dimension, greatest iteration time
Number is Maxgen, and exploration step number is T, and the variable neighborhood value of i-th of body jth dimension is Rji∈[Rmin,Rmax].It is according to these settings
Initialize installation can be carried out to population position：
xtji=x0ji-Δxtji+2Δxtji*randomtji(0,1) (7)
X0jiIt is the initial value randomly generated：
Δxtji=Rji*(Xmax-Xmin)*randomtji(0,1) (8)
(2) each individual is gone into action, and individual i carries out T step detections every time, and the renewal of position is determined by following formula：
x0ji=Xmini+(Xmaxi-Xmini)*randomji(0,1) (9)
In exploration behavior once, individual exploration behavior is represented by：
F (x in formulatji) it is the position that each individual is completed to mark after search.
Pheromones are defined as follows：
And sensitivity updates according to equation below：
S in formulaminAnd SmaxIt is the minimum value and maximum of sensitivity respectively, the minimum value and maximum of pheromones are respectively
PminAnd Pmax, and P is provided hereinmin=SminAnd Pmin=Smax。
After the search of epicycle terminates, according to the starting point of equation below renewal next round search：
(3) the termination strategy of algorithm has 3 kinds of situations：
A) object function has reached its globally optimal solution, i.e. fmax≥fopt；
B) current iteration number g alreadys exceed greatest iteration number Maxgen；
C) any one above-mentioned situation is met.Otherwise repeat step (2).
As a kind of preferable scheme：The flexible measurement method is further comprising the steps of：4), periodically by offline analysis data
It is input in training set, updates RBF neural network model.
The method specific implementation step of the present embodiment is as follows：
Step 1：To radar object 1, according to specificity analysis and climatic analysis, selection operation variable and easily survey variable are as mould
The input of type.Performance variable and easily survey variable are obtained by spot database 4.
Step 2：Sample data is pre-processed, completed by data preprocessing module 7.
Step 3：Initial R BF neural network models 8 are established based on model training sample data.Input data such as step 2 institute
Acquisition is stated, output data is obtained by offline chemical examination.
Step 4：Optimize the parameter of Initial R BF neural network models by free search algorithm module 9.
Step 5：Offline analysis data is periodically input in training set by model modification module 10, updates RBF neural
Model, the optimal hard measurement host computer 5 based on free searching algorithm optimization RBF neural network model, which is established, to be completed.
Step 6：Sea clutter hard measurement value display instrument 6 is shown based on free searching algorithm optimization RBF neural network model
The output of optimal hard measurement host computer 5, complete the display to the optimal hard measurement of sea clutter.
Based on free searching algorithm optimization RBF neural optimal hard measurement host computer, including data preprocessing module,
RBF neural network modules, model modification module, and provide a kind of flexible measurement method realized with soft measuring instrument.This hair
The bright online optimal hard measurement for realizing sea clutter, randomness caused by overcoming human factor influence, and improve model prediction
Stability, reduce the possibility that model prediction is absorbed in local optimum.
Claims (2)
1. a kind of sea clutter optimal soft survey instrument based on free searching algorithm optimization RBF neural, described is optimal soft
Measuring instrumentss, including radar, for measuring easily survey the field intelligent instrument of variable, the control station for measuring performance variable, deposit
Put the spot database and sea clutter hard measurement value display instrument of data；The field intelligent instrument, control station and radar connect,
The field intelligent instrument, control station and spot database connect, it is characterised in that：The soft measuring instrument also includes being based on certainly
By the optimal hard measurement host computer of searching algorithm optimization RBF neural, the spot database is based on freely searching for described
The input connection of the optimal hard measurement host computer of algorithm optimization RBF neural, it is described based on free searching algorithm optimization RBF
The output end of the optimal hard measurement host computer of neutral net is connected with sea clutter hard measurement value display instrument；It is described to be based on freely searching for
The optimal hard measurement host computer of algorithm optimization RBF neural includes：
Data preprocessing module, for the model training inputted from spot database sample to be pre-processed, to training sample
Centralization, that is, the average value of sample is subtracted, then it is standardized：
Calculate average：
Calculate variance：
Standardization：
Wherein, TX is training sample, and N is number of training,For the average of training sample, X is the training sample after standardization,
σxTo calculate variance.
Using feedforward network topological structure, each unit output h of hidden layer is:
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<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein w is connection weight, and h is the unit output of hidden layer.
Free search algorithm module, for being optimized using free searching algorithm to the kernel function width of RBF neural, have
Body step is as follows：
(1) the scale n and dimension m, [X of population are initializedmini, Xmaxi] excursion as i-th dimension, maximum iteration is
Maxgen, exploration step number are T, and the variable neighborhood value of i-th of body jth dimension is Rji∈[Rmin,Rmax].Can be right according to these settings
Population position x carries out Initialize installation：
xtji=x0ji-Δxtji+2Δxtji*randomtji(0,1) (7)
X0jiIt is the initial value randomly generated：
Δxtji=Rji*(Xmax-Xmin)*randomtji(0,1) (8)
(2) each individual is gone into action, and individual i carries out T step detections every time, and the renewal of position is determined by following formula：
x0ji=Xmini+(Xmaxi-Xmini)*randomji(0,1) (9)
In exploration behavior once, individual exploration behavior is represented by：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>t</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>f</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>t</mi>
<mi>j</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mi>max</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>t</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
F (x in formulatji) it is the position that each individual is completed to mark after search.
Pheromones P is defined as follows：
<mrow>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
And sensitivity S updates according to equation below：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<msub>
<mi>S</mi>
<mi>min</mi>
</msub>
<mo>+</mo>
<msub>
<mi>&Delta;S</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;S</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>S</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msub>
<mi>S</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>random</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
S in formulaminAnd SmaxIt is the minimum value and maximum of sensitivity respectively, the minimum value and maximum of pheromones are respectively Pmin
And Pmax, and P is provided hereinmin=SminAnd Pmin=Smax。
After the search of epicycle terminates, according to the starting point of equation below renewal next round search：
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mn>0</mn>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mn>0</mn>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo><</mo>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
(3) the termination strategy of algorithm has 3 kinds of situations：
A) object function has reached its globally optimal solution, i.e. fmax≥fopt；
B) current iteration number g alreadys exceed greatest iteration number Maxgen；
C) any one above-mentioned situation is met.Otherwise repeat step (2).
Model modification module, for the online updating of model, periodically offline analysis data is input in training set, updates RBF
Neural network model.
A kind of 2. optimal hard measurement of sea clutter as claimed in claim 1 based on free searching algorithm optimization RBF neural
The flexible measurement method that instrument is realized, it is characterised in that：The flexible measurement method comprises the following steps：
1), to radar object, according to specificity analysis and climatic analysis, selection operation variable and easily survey variable are as the defeated of model
Enter, performance variable and easily survey variable are obtained by spot database；
2), the model training sample inputted from spot database is pre-processed, to training sample centralization, that is, subtracts sample
Average value, then it is standardized so that its average be 0, variance 1.The processing is using following formula process come complete
Into：
2.1) average is calculated：
2.2) variance is calculated：
2.3) standardize：
Wherein, TX is training sample, and N is number of training,For the average of training sample, X is the training sample after standardization,
σxTo calculate variance.
3) to the training sample for being transmitted through coming from data preprocessing module, built using RBF neural.RBF neural is used
RBF forms implicit sheaf space as " base " of hidden unit, and defeated people's vector is mapped directly into implicit sheaf space and implied
The mapping in sheaf space to output space is linear.In general, network is nonlinear by the mapping for being input to output, and
Network output is for adjustable parameter but is linear.Theory is it has been proved that RBF networks have global and optimal Approximation
Can, it is that the optimal network of mapping function is completed in feedforward neural network.As long as there is enough hidden neurons, RBF can with appoint
The approximate any continuous function of precision of anticipating.Using feedforward network topological structure, each unit output h of hidden layer is:
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>h</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>R</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>X</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>R</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mo>|</mo>
<mo>|</mo>
<mi>X</mi>
<mo>-</mo>
<mi>C</mi>
<mo>|</mo>
<mo>|</mo>
<mo>/</mo>
<msub>
<mi>&sigma;</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>3</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>L</mi>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, X is N-dimensional input vector, CiIt is the vector with X with dimension, RiFor Radial basis kernel function.
Ri() generally use Gaussian function, wherein σ are kernel function width, i.e.,：
<mrow>
<msub>
<mi>R</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>X</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mo>|</mo>
<mo>|</mo>
<mi>X</mi>
<mo>-</mo>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mo>/</mo>
<msubsup>
<mi>&sigma;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Therefore the output y of network output layer is
<mrow>
<mi>y</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>l</mi>
</munderover>
<msub>
<mi>w</mi>
<mi>j</mi>
</msub>
<mi>h</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein w is connection weight, and h is the unit output of hidden layer.
4), the kernel function width of RBF neural is optimized using free searching algorithm, comprised the following steps that：
(1) the scale n and dimension m, [X of population are initializedmini, Xmaxi] excursion as i-th dimension, maximum iteration is
Maxgen, exploration step number are T, and the variable neighborhood value of i-th of body jth dimension is Rji∈[Rmin,Rmax].Can be right according to these settings
Population position carries out Initialize installation：
xtji=x0ji-Δxtji+2Δxtji*randomtji(0,1) (7)
X0jiIt is the initial value randomly generated：
Δxtji=Rji*(Xmax-Xmin)*randomtji(0,1) (8)
(2) each individual is gone into action, and individual i carries out T step detections every time, and the renewal of position is determined by following formula：
x0ji=Xmini+(Xmaxi-Xmini)*randomji(0,1) (9)
In exploration behavior once, individual exploration behavior is represented by：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mrow>
<mi>t</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>f</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>t</mi>
<mi>j</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mi>max</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>t</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
F (x in formulatji) it is the position that each individual is completed to mark after search.
Pheromones P is defined as follows：
<mrow>
<msub>
<mi>P</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
And sensitivity S updates according to equation below：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<msub>
<mi>S</mi>
<mi>min</mi>
</msub>
<mo>+</mo>
<msub>
<mi>&Delta;S</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;S</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>S</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msub>
<mi>S</mi>
<mi>min</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>random</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
S in formulaminAnd SmaxIt is the minimum value and maximum of sensitivity respectively, the minimum value and maximum of pheromones are respectively Pmin
And Pmax, and P is provided hereinmin=SminAnd Pmin=Smax。
After the search of epicycle terminates, according to the starting point of equation below renewal next round search：
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mn>0</mn>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mn>0</mn>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mo>&prime;</mo>
</msubsup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo><</mo>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>S</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
(3) the termination strategy of algorithm has 3 kinds of situations：
A) object function has reached its globally optimal solution, i.e. fmax≥fopt；
B) current iteration number g alreadys exceed greatest iteration number Maxgen；
C) any one above-mentioned situation is met.Otherwise repeat step (2).
Periodically offline analysis data is input in training set, updates RBF neural network model.
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| CN111310389A (en) * | 2020-03-19 | 2020-06-19 | 石家庄铁道大学 | Photoacoustic cell temperature compensation method and system based on FA optimized RBF |
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