【SVM预测】基于引力搜索算法改进SVM实现数据回归预测Matlab代码

1 简介

为了提高支持向量机(SVM)模型的拟合精度和泛化能力,以最小化输出量的均方误差为目标,采用基于万有引力定律的优化机制,提出了一种基于引力搜索算法的SVM参数优化方法.通过仿真实验验证,基于引力搜索算法的SVM回归模型不但精度高且泛化能力强.将该方法应用于谷氨酸发酵过程的建模研究,仿真结果表明,该方法可以提高谷氨酸质量浓度的预测精度.

 

2 部分代码

% GSA code v1.0.% Generated by Esmat Rashedi, 2009. % Adopted from: "  E. Rashedi, H. Nezamabadi-pour and S. Saryazdi,% 揋SA: A Gravitational Search Algorithm? Information sciences, vol. 179,% no. 13, pp. 2232-2248, 2009."​% Gravitational Search Algorithm.function BestChart=GSA(F_index,N,max_it,ElitistCheck,chaosIndex,chValueInitial)​%V:   Velocity.%a:   Acceleration.%M:   Mass.  Ma=Mp=Mi=M;%dim: Dimension of test function.%N:   Number of agents.%X:   Position of agents. dim-by-N matrix.%R:   Distance between agents in search space.%[low-up]: Allowable range for search space.%Rnorm:  Norm in eq.8.%Rpower: Power of R in eq.7. G_History=zeros(1,max_it); Rnorm=2;  Rpower=1;  min_flag=1; % 1: minimization, 0: maximization %get allowable range and dimension of the test function.[low,up,dim]=test_functions_range(F_index); ​%random initialization for agents.X=initialization(dim,N,up,low); ​%create chart of best so far and average fitnesses.BestChart=[];MeanChart=[];​V=zeros(dim,N);wMax=chValueInitial;wMin=1e-10;for iteration=1:max_it    chValue=wMax-iteration*((wMax-wMin)/max_it);​%     iteration        %Checking allowable range.     X=space_bound(X,up,low); ​    %Evaluation of agents.     fitness=evaluateF(X,F_index);         [best best_X]=min(fitness); %min: minimization.  max: maximization.        if iteration==1       Fbest=best;Lbest=X(:,best_X);    end    if best<Fbest  % < : minimization. > : maximization       Fbest=best;Lbest=X(:,best_X);    end      BestChart=[BestChart Fbest];MeanChart=[MeanChart mean(fitness)];​%Calculation of M. eq.14-20[M]=massCalculation(fitness,min_flag); ​%Calculation of Gravitational constant. eq.13.G=Gconstant(iteration,max_it);%G=chaos(3,iteration,max_it,10);switch chaosIndex    case 1        G=Gconstant(iteration,max_it);     case 2        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 3        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 4        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 5        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 6        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 7        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 8        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 9        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 10        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);    case 11        G=G+chaos(chaosIndex-1,iteration,max_it,chValue);endG_History(iteration)=G;test_G(iteration)=G;if iteration==499    alisop=0;end%Calculation of accelaration in gravitational field. eq.7-10,21.a=Gfield(M,X,G,Rnorm,Rpower,ElitistCheck,iteration,max_it);​%Agent movement. eq.11-12[X,V]=move(X,a,V);​​end %iteration​​

3 仿真结果

4 参考文献

[1]戴娟, 顾斌杰, 潘丰. 基于引力搜索算法的SVM参数优化及应用[J]. 服装学报, 2013, 12(002):127-131.

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