MATLAB优化与控制模型代码 GA优化后的RBF神经网络优化分析代码.zip

clear all close all G = 15; Size = 30; CodeL = 10; for i = 1:3 MinX(i) = 0.1*ones(1); MaxX(i) = 3*ones(1); end for i = 4:1:9 MinX(i) = -3*ones(1); MaxX(i) = 3*ones(1); end for i = 10:1:12 MinX(i) = -ones(1); MaxX(i) = ones(1); end E = round(rand(Size,12*CodeL)); %Initial Code! BsJ = 0; for kg = 1:1:G time(kg) = kg for s = 1:1:Size m = E(s,:); for j = 1:1:12 y(j) = 0; mj = m((j-1)*CodeL + 1:1:j*CodeL); for i = 1:1:CodeL y(j) = y(j) + mj(i)*2^(i-1); end f(s,j) = (MaxX(j) - MinX(j))*y(j)/1023 + MinX(j); end % ************Step 1:Evaluate BestJ ******************* p = f(s,:); [p,BsJ] = RBF(p,BsJ); BsJi(s) = BsJ; end [OderJi,IndexJi] = sort(BsJi); BestJ(kg) = OderJi(1); BJ = BestJ(kg); Ji = BsJi+1e-10; fi = 1./Ji; [Oderfi,Indexfi] = sort(fi); Bestfi = Oderfi(Size); BestS = E(Indexfi(Size),:); % ***************Step 2:Select and Reproduct Operation********* fi_sum = sum(fi); fi_Size = (Oderfi/fi_sum)*Size; fi_S = floor(fi_Size); kk = 1; for i = 1:1:Size for j = 1:1:fi_S(i) TempE(kk,:) = E(Indexfi(i),:); kk = kk + 1; end end % ****************Step 3:Crossover Operation******************* pc = 0.60; n = ceil(20*rand); for i = 1:2:(Size - 1) temp = rand; if pc>temp for j = n:1:20 TempE(i,j) = E(i+1,j); TempE(i+1,j) = E(i,j); end end end TempE(Size,:) = BestS; E = TempE; %*****************Step 4:Mutation Operation********************* pm = 0.001 - [1:1:Size]*(0.001)/Size; for i = 1:1:Size for j = 1:1:12*CodeL temp = rand; if pm>temp if TempE(i,j) == 0 TempE(i,j) = 1; else TempE(i,j) = 0; end end end end %Guarantee TempE(Size,:) belong to the best individual TempE(Size,:) = BestS; E = TempE; %******************************************************************** end Bestfi BestS fi Best_J = BestJ(G) figure(1); plot(time,BestJ); xlabel('Times');ylabel('BestJ'); save pfile p;