CN107942303A - A kind of Intelligent radar sea clutter forecast system and method based on improvement artificial bee colony algorithm - Google Patents
A kind of Intelligent radar sea clutter forecast system and method based on improvement artificial bee colony algorithm Download PDFInfo
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Abstract
The invention discloses a kind of based on the Intelligent radar sea clutter forecast system and method that improve artificial bee colony algorithm, system is sequentially connected by radar, database and host computer and formed, radar is irradiated detected marine site, and by radar sea clutter data storage to the database, the host computer includes data preprocessing module, robust forecasting model modeling module, intelligent optimizing module, sea clutter forecast module, discrimination model update module and result display module.The present invention is directed to the chaotic characteristic of radar sea clutter, radar sea clutter data are reconstructed, and nonlinear fitting is carried out to the data after reconstruct, introduces and improves artificial bee colony method, so as to establish the intelligent prediction model of radar sea clutter, so as to on-line prediction radar sea clutter.Modeling method used in the present invention only needs less sample；And reduce the influence of human factor, and intelligent height, strong robustness.
Description
Technical field
The present invention relates to radar data process field, especially, is related to a kind of based on the intelligence for improving artificial bee colony algorithm
Radar sea clutter forecast system and method.
Background technology
Sea clutter, that is, come from the backscattering echo on a piece of sea irradiated by radar emission signal.Due to sea clutter
To " point " target from sea or close to sea, such as maritime buoyage and the radar return of the afloat ice cube target of floating
Detectability forms serious restriction, therefore the research of sea clutter has very the detection performance of the targets such as steamer in marine background
Important influence is so as to have most important theories meaning and practical value.
Traditionally sea clutter is considered as single random process, and such as logarithm normal distribution, K are distributed.But these models exist
There is its specific limitation in practical application, one of major reason is that sea clutter seems random waveform, actually simultaneously
Without random distribution nature.
The content of the invention
In order to overcome traditional radar data handle easily be affected by human factors, it is intelligent deficiency the shortcomings that, the present invention carries
Human factor influence, the intelligent high Intelligent radar sea clutter forecast system based on improvement artificial bee colony algorithm are avoided for a kind of
And method.
The technical solution adopted by the present invention to solve the technical problems is：
It is a kind of based on improve artificial bee colony algorithm Intelligent radar sea clutter forecast system, including radar, database and
Host computer, radar, database and host computer are sequentially connected, and the radar is irradiated detected marine site, and Radar Sea is miscellaneous
Wave number includes according to the database, the host computer is stored into：
Data preprocessing module, to carry out radar sea clutter data prediction, is completed using following process：
(1) radar is irradiated detected marine site, and by radar sea clutter data storage to the database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalization amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and the value range of D ＜ N, D are 50-70；
The robust forecasting model modeling module, to establish forecasting model, is completed using following process：
X, Y that data preprocessing module is obtained substitute into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixes turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be support vector machines kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
The intelligence optimizing module, is to the nuclear parameter θ of robust forecasting model and punishment using artificial bee colony algorithm is improved
Number γ is optimized, and is completed using following process：
Step 1：Initialization improves the parameter of artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud；The feasible solution of the positional representation problem in nectar source, since model has two parameter needs
Optimization, so position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number
Iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
Step 2：For nectar source piDistribution one leads bee, scans for as the following formula, produces new nectar source Vi；
Step 3：Calculate ViFitness value, according to sensitivity and the method choice nectar source of pheromone ligand, its process is such as
Under：
1) fitness value in P nectar source is calculated；
2) the pheromones nf (i) in i-th of nectar source is calculated：
3) i-th of sensitivity S (i)~U (0,1) for following bee is randomly generated；
4) nectar source for coordinating i-th of the sensitivity for following bee is found out：I is found out at random, meets nf (i)≤S (i).
Step 4：Calculate lead the nectar source that bee is found by more with probability；
Step 5：Bee is followed using with being scanned for by the way of leading bee identical, according to sensitivity and the side of pheromone ligand
Method selects nectar source；
Step 6：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead bee role to be changed into scouting
Bee, otherwise passes directly to step 8；
Step 7：Search bee randomly generates new nectar source；
Step 8：Iter=iter+1, judges whether to have been maxed out iterations, and optimized parameter is exported if meeting,
Otherwise step 2 is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
Sea clutter forecast module, to carry out sea clutter prediction, is completed using following process：
1) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent the
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
2) it is normalized；
3) substitute into the function f (x) to be estimated that robust forecasting model modeling module obtains and sampling instant (t+1) is calculated
Sea clutter predicted value.
The discrimination model update module, to by the sampling time interval of setting, gathered data, the actual measurement number that will be obtained
According to compared with model prediction value, if relative error is more than 10%, new data is added into training sample data, renewal forecast mould
Type.
The result display module, the predicted value sea clutter forecast module to be calculated are shown in host computer.
It is a kind of pre- based on radar sea clutter used in the Intelligent radar sea clutter forecast system of artificial bee colony algorithm is improved
Reporting method, the method comprise the following steps：
(1) radar is irradiated detected marine site, and by radar sea clutter data storage to the database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample i=1 ..., N；
(3) training sample is normalized, obtains normalization amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and the value range of D ＜ N, D are 50-70；
(5) obtained X, Y are substituted into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,K=exp (- | | xi-xj||/
θ2), the transposition of subscript T representing matrixes,It is Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b*It is amount of bias,With exp (- | | x-xi||/θ2) be support vector machines kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
(6) the nuclear parameter θ and penalty coefficient γ of step (5) are optimized with improvement artificial bee colony algorithm, using as follows
Process is completed：
Step 1：Initialization improves the parameter of artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud；The feasible solution of the positional representation problem in nectar source, since model has two parameter needs
Optimization, so position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number
Iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
Step 2：For nectar source piDistribution one leads bee, scans for as the following formula, produces new nectar source Vi；
Step 3：Calculate ViFitness value, according to sensitivity and the method choice nectar source of pheromone ligand, its process is such as
Under：
1) fitness value in P nectar source is calculated；
2) the pheromones nf (i) in i-th of nectar source is calculated：
3) i-th of sensitivity S (i)~U (0,1) for following bee is randomly generated；
4) nectar source for coordinating i-th of the sensitivity for following bee is found out：I is found out at random, meets nf (i)≤S (i).
Step 4：Calculate lead the nectar source that bee is found by more with probability；
Step 5：Bee is followed using with being scanned for by the way of leading bee identical, according to sensitivity and the side of pheromone ligand
Method selects nectar source；
Step 6：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead bee role to be changed into scouting
Bee, otherwise passes directly to step 8；
Step 7：Search bee randomly generates new nectar source；
Step 8：Iter=iter+1, judges whether to have been maxed out iterations, and optimized parameter is exported if meeting,
Otherwise step 2 is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
(7) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], TX represents sea
Signal amplitude matrix of the clutter from t-D+1 sampling instants to t sampling instants, xt-D+1Represent the sea of t-D+1 sampling instants
Clutter echo-signal amplitude, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
(8) it is normalized；
(9) the sea clutter forecast that sampling instant (t+1) is calculated in the function f (x) to be estimated that step (5) obtains is substituted into
Value.
(10) the sampling time interval gathered data of setting is pressed, by obtained measured data compared with model prediction value, such as
Fruit relative error is more than 10%, then new data is added training sample data, updates forecasting model.
The present invention technical concept be：The present invention be directed to radar sea clutter chaotic characteristic, to radar sea clutter data into
Row reconstruct, and nonlinear fitting is carried out to the data after reconstruct, introduce and improve artificial bee colony algorithm method, so as to establish Radar Sea
The intelligent prediction model of clutter.
Beneficial effects of the present invention are mainly manifested in：1st, radar sea clutter forecasting model is established, can be with on-line prediction thunder
Up to sea clutter；2nd, modeling method used only needs less sample；3rd, the influence of human factor, intelligent height, Shandong are reduced
Rod is strong.
Brief description of the drawings
Fig. 1 is the hardware structure diagram of system proposed by the invention；
Fig. 2 is the functional block diagram of host computer proposed by the invention.
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
It is a kind of based on the Intelligent radar sea clutter forecast system for improving artificial bee colony algorithm, including radar with reference to Fig. 1, Fig. 2
The database 2 and host computer 3 of 1 connection, radar 1, database 2 and host computer 3 are sequentially connected, and the radar 1 is to detected marine site
It is irradiated, and radar sea clutter data storage to the database 2, the host computer 3 is included：
Data preprocessing module 4, to carry out radar sea clutter data prediction, is completed using following process：
(1) radar is irradiated detected marine site, and by radar sea clutter data storage to the database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalization amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and the value range of D ＜ N, D are 50-70；
The robust forecasting model modeling module 5, to establish forecasting model, is completed using following process：
X, Y that data preprocessing module is obtained substitute into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixes turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be support vector machines kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
The intelligence optimizing module 6, to the nuclear parameter θ of robust forecasting model and to be punished using improvement artificial bee colony algorithm
Penalty factor γ is optimized, and is completed using following process：
Step 1：Initialization improves the parameter of artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud；The feasible solution of the positional representation problem in nectar source, since model has two parameter needs
Optimization, so position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number
Iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
Step 2：For nectar source piDistribution one leads bee, scans for as the following formula, produces new nectar source Vi；
Step 3：Calculate ViFitness value, according to sensitivity and the method choice nectar source of pheromone ligand, its process is such as
Under：
1) fitness value in P nectar source is calculated；
2) the pheromones nf (i) in i-th of nectar source is calculated：
3) i-th of sensitivity S (i)~U (0,1) for following bee is randomly generated；
4) nectar source for coordinating i-th of the sensitivity for following bee is found out：I is found out at random, meets nf (i)≤S (i).
Step 4：Calculate lead the nectar source that bee is found by more with probability；
Step 5：Bee is followed using with being scanned for by the way of leading bee identical, according to sensitivity and the side of pheromone ligand
Method selects nectar source；
Step 6：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead bee role to be changed into scouting
Bee, otherwise passes directly to step 8；
Step 7：Search bee randomly generates new nectar source；
Step 8：Iter=iter+1, judges whether to have been maxed out iterations, and optimized parameter is exported if meeting,
Otherwise step 2 is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
Sea clutter forecast module 7, to carry out sea clutter prediction, is completed using following process：
1) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent the
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
2) it is normalized；
3) substitute into the function f (x) that robust forecasting model modeling module obtains and obtain the sea clutter forecast of sampling instant (t+1)
Value.
Discrimination model update module 8, to by the sampling time interval gathered data of setting, by obtained measured data with
Model prediction value compares, if relative error is more than 10%, new data is added training sample data, updates forecasting model.
Result display module 9, the predicted value sea clutter forecast module to be calculated are shown in host computer.
The hardware components of the host computer 3 include：I/O elements, for the collection of data and the transmission of information；Data store
Device, data sample and operating parameter needed for storage running etc.；The software program of function module is realized in program storage, storage；
Arithmetic unit, executive program, realizes the function of specifying；Display module, shows the parameter and operation result of setting.
Embodiment 2
It is a kind of based on the Intelligent radar sea clutter forecasting procedure for improving artificial bee colony algorithm, the side with reference to Fig. 1, Fig. 2
Method comprises the following steps：
(1) radar is irradiated detected marine site, and by radar sea clutter data storage to the database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalization amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and the value range of D ＜ N, D are 50-70；
(5) obtained X, Y are substituted into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixes turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be support vector machines kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
(6) the nuclear parameter θ and penalty coefficient γ of step 4) are optimized with improvement artificial bee colony algorithm, using following mistake
Journey is completed：
Step 1：Initialization improves the parameter of artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud；The feasible solution of the positional representation problem in nectar source, since model has two parameter needs
Optimization, so position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number
Iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
Step 2：For nectar source piDistribution one leads bee, scans for as the following formula, produces new nectar source Vi；
Step 3：Calculate ViFitness value, according to sensitivity and the method choice nectar source of pheromone ligand, its process is such as
Under：
1) fitness value in P nectar source is calculated；
2) the pheromones nf (i) in i-th of nectar source is calculated：
3) i-th of sensitivity S (i)~U (0,1) for following bee is randomly generated；
4) nectar source for coordinating i-th of the sensitivity for following bee is found out：I is found out at random, meets nf (i)≤S (i).
Step 4：Calculate lead the nectar source that bee is found by more with probability；
Step 5：Bee is followed using with being scanned for by the way of leading bee identical, according to sensitivity and the side of pheromone ligand
Method selects nectar source；
Step 6：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead bee role to be changed into scouting
Bee, otherwise passes directly to step 8；
Step 7：Search bee randomly generates new nectar source；
Step 8：Iter=iter+1, judges whether to have been maxed out iterations, and optimized parameter is exported if meeting,
Otherwise step 2 is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
(7) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
(8) it is normalized；
(9) the sea clutter forecast that sampling instant (t+1) is calculated in the function f (x) to be estimated that step (5) obtains is substituted into
Value；
(10) the sampling time interval gathered data of setting is pressed, by obtained measured data compared with model prediction value, such as
Fruit relative error is more than 10%, then new data is added training sample data, updates forecasting model.
, can be miscellaneous with on-line prediction Radar Sea by above example as it can be seen that the present invention establishes radar sea clutter forecasting model
Ripple；And modeling method used only needs less sample；In addition, reduce the influence of human factor, and intelligent height, robustness
By force.
Claims (2)
1. it is a kind of based on improve artificial bee colony algorithm Intelligent radar sea clutter forecast system, including radar, database and on
Position machine, radar, database and host computer are sequentially connected, it is characterised in that：The radar is irradiated detected marine site, and will
Radar sea clutter data storage includes data preprocessing module, robust forecasting model to the database, the host computer
Modeling module, intelligent optimizing module, sea clutter forecast module, discrimination model update module and result display module：
The data preprocessing module, to carry out radar sea clutter data prediction, is completed using following process：
(1) radar is irradiated detected marine site, and by radar sea clutter data storage to the database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalization amplitude
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(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
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Wherein, D represents reconstruct dimension, and D is natural number, and the value range of D ＜ N, D are 50-70；
The robust forecasting model modeling module is completed to establish forecasting model using following process：
X, Y that data preprocessing module is obtained substitute into following linear equation：
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<mo>*</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>&alpha;</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>Y</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
<mrow>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msup>
<msub>
<mi>&alpha;</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
</msup>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mo>|</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
<mo>/</mo>
<msup>
<mi>&theta;</mi>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msup>
<mi>b</mi>
<mo>*</mo>
</msup>
</mrow>
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,The transposition of subscript T representing matrixes,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be support vector machines kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
The intelligence optimizing module, to be to the nuclear parameter θ of robust forecasting model and punishment using improvement artificial bee colony algorithm
Number γ is optimized, and is completed using following process：
(A)：Initialization improves the parameter of artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initial ranging space
Minimum value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, since model has two parameters to need to optimize,
So position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number iter
=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
(B)：For nectar source piDistribution one leads bee, scans for as the following formula, produces new nectar source Vi；
(C)：Calculate ViFitness value, it is as follows according to sensitivity and the method choice nectar source of pheromone ligand, its process：
Calculate the fitness value in P nectar source；
(C1) the pheromones nf (i) in i-th of nectar source is calculated：
(C2) i-th of sensitivity S (i)~U (0,1) for following bee is randomly generated；
(C3) nectar source for coordinating i-th of the sensitivity for following bee is found out：I is found out at random, meets nf (i)≤S (i).
(D)：Calculate lead the nectar source that bee is found by more with probability；
(E)：Bee is followed using with being scanned for by the way of leading bee identical, according to sensitivity and the method choice of pheromone ligand
Nectar source；
(F)：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead bee role to be changed into search bee, otherwise
Pass directly to step H；
(G)：Search bee randomly generates new nectar source；
(H)：Iter=iter+1, judges whether to have been maxed out iterations, exports optimized parameter if meeting, otherwise turns
To step (B).
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
Sea clutter forecast module, to carry out sea clutter prediction, is completed using following process：
(a) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent t-D
The sea clutter echo-signal amplitude of+1 sampling instant, xtRepresent the sea clutter echo-signal amplitude of t sampling instants, TX represents sea
Signal amplitude matrix of the clutter from t-D+1 sampling instants to t sampling instants；
(b) it is normalized；
<mrow>
<mover>
<mrow>
<mi>T</mi>
<mi>X</mi>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>=</mo>
<mfrac>
<mrow>
<mi>T</mi>
<mi>X</mi>
<mo>-</mo>
<mi>min</mi>
<mi>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</mi>
<mi>x</mi>
</mrow>
</mfrac>
</mrow>
(c) sea that sampling instant (t+1) is calculated in the function f (x) to be estimated that robust forecasting model modeling module obtains is substituted into
Clutter predicted value.
The discrimination model update module, to by the sampling time interval gathered data of setting, by obtained measured data with
Model prediction value compares, if relative error is more than 10%, new data is added training sample data, updates forecasting model.
The result display module, the predicted value sea clutter forecast module to be calculated are shown in host computer.
2. used in the Intelligent radar sea clutter forecast system based on improvement artificial bee colony algorithm described in a kind of claim 1
Radar sea clutter forecasting procedure, it is characterised in that：The method comprises the following steps：
(1) radar is irradiated detected marine site, and by radar sea clutter data storage to the database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalization amplitude
<mrow>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mi>min</mi>
<mi>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</mi>
<mi>x</mi>
</mrow>
</mfrac>
</mrow>
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
<mrow>
<mi>Y</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>D</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>D</mi>
<mo>+</mo>
<mn>2</mn>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>N</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein, D represents reconstruct dimension, and D is natural number, and the value range of D ＜ N, D are 50-70；
(5) obtained X, Y are substituted into following linear equation：
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msubsup>
<mn>1</mn>
<mi>v</mi>
<mi>T</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mn>1</mn>
<mi>v</mi>
</msub>
</mtd>
<mtd>
<mrow>
<mi>K</mi>
<mo>+</mo>
<msub>
<mi>V</mi>
<mi>&gamma;</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msup>
<mi>b</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>&alpha;</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>Y</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
<mrow>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msup>
<msub>
<mi>&alpha;</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
</msup>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mo>|</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
<mo>/</mo>
<msup>
<mi>&theta;</mi>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msup>
<mi>b</mi>
<mo>*</mo>
</msup>
</mrow>
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,The transposition of subscript T representing matrixes,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be support vector machines kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
(6) the nuclear parameter θ and penalty coefficient γ of step (5) are optimized with improvement artificial bee colony algorithm, using following process
Complete：
(6.1) initialization improves the parameter of artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initial ranging space
Minimum value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, since model has two parameters to need to optimize,
So position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number iter
=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
(6.2) it is nectar source piDistribution one leads bee, scans for as the following formula, produces new nectar source Vi；
(6.3) V is calculatediFitness value, it is as follows according to sensitivity and the method choice nectar source of pheromone ligand, its process：
1) fitness value in P nectar source is calculated；
2) the pheromones nf (i) in i-th of nectar source is calculated：
3) i-th of sensitivity S (i)~U (0,1) for following bee is randomly generated；
4) nectar source for coordinating i-th of the sensitivity for following bee is found out：I is found out at random, meets nf (i)≤S (i).
(6.4) calculate lead the nectar source that bee is found by more with probability；
(6.5) bee is followed to be selected using with being scanned for by the way of leading bee identical according to the method for sensitivity and pheromone ligand
Select nectar source；
(7) nectar source V is judgediWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead bee role to be changed into search bee, otherwise directly
It is switched to step (9)；
(8) search bee randomly generates new nectar source；
(9) iter=iter+1, judges whether to have been maxed out iterations, exports optimized parameter if meeting, otherwise turns
To step (3).
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
(10) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent t-
The sea clutter echo-signal amplitude of D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
(11) it is normalized；
<mrow>
<mover>
<mrow>
<mi>T</mi>
<mi>X</mi>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>=</mo>
<mfrac>
<mrow>
<mi>T</mi>
<mi>X</mi>
<mo>-</mo>
<mi>min</mi>
<mi>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</mi>
<mi>x</mi>
</mrow>
</mfrac>
</mrow>
(12) the sea clutter predicted value that sampling instant (t+1) is calculated in the function f (x) to be estimated that step (5) obtains is substituted into.
(13) by the sampling time interval gathered data of setting, by obtained measured data compared with model prediction value, if phase
10% is more than to error, then new data is added into training sample data, updates forecasting model.
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