麻雀搜索算法（SSA）优化bp网络.zip

% 清空环境 clc clear %读取数据 load data input output %节点个数 inputnum=2; hiddennum=5; outputnum=1; %训练数据和预测数据 input_train=input(1:1900,:)'; input_test=input(1901:2000,:)'; output_train=output(1:1900)'; output_test=output(1901:2000)'; %选连样本输入输出数据归一化 [inputn,inputps]=mapminmax(input_train); [outputn,outputps]=mapminmax(output_train); %构建网络 net=newff(inputn,outputn,hiddennum); % 参数初始化 dim=21; maxgen=30; % 进化次数 sizepop=20; %种群规模 popmax=5; popmin=-5; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% P_percent = 0.2; % The population size of producers accounts for "P_percent" percent of the total population size pNum = round( sizepop * P_percent ); % The population size of the producers %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i=1:sizepop pop(i,:)=5*rands(1,21); % V(i,:)=rands(1,21); fitness(i)=fun(pop(i,:),inputnum,hiddennum,outputnum,net,inputn,outputn); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pFit = fitness; [ fMin, bestI ] = min( fitness ); % fMin denotes the global optimum fitness value bestX = pop( bestI, : ); % bestX denotes the global optimum position corresponding to fMin %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%麻雀搜索算法 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for t = 1 : maxgen [ ans, sortIndex ] = sort( pFit );% Sort. [fmax,B]=max( pFit ); worse= pop(B,:); r2=rand(1); if(r2<0.8) for i = 1 : pNum % Equation (3) r1=rand(1); pop( sortIndex( i ), : ) = pop( sortIndex( i ), : )*exp(-(i)/(r1*maxgen)); fitness(sortIndex( i ))=fun(pop(sortIndex( i ),:),inputnum,hiddennum,outputnum,net,inputn,outputn); end else for i = 1 : pNum pop( sortIndex( i ), : ) = pop( sortIndex( i ), : )+randn(1)*ones(1,dim); fitness(sortIndex( i ))=fun(pop(sortIndex( i ),:),inputnum,hiddennum,outputnum,net,inputn,outputn); end end [ fMMin, bestII ] = min( fitness ); bestXX = pop( bestII, : ); for i = ( pNum + 1 ) : sizepop % Equation (4) A=floor(rand(1,dim)*2)*2-1; if( i>(sizepop/2)) pop( sortIndex(i ), : )=randn(1)*exp((worse-pop( sortIndex( i ), : ))/(i)^2); else pop( sortIndex( i ), : )=bestXX+(abs(( pop( sortIndex( i ), : )-bestXX)))*(A'*(A*A')^(-1))*ones(1,dim); end fitness(sortIndex( i ))=fun(pop(sortIndex( i ),:),inputnum,hiddennum,outputnum,net,inputn,outputn); end c=randperm(numel(sortIndex)); b=sortIndex(c(1:3)); for j = 1 : length(b) % Equation (5) if( pFit( sortIndex( b(j) ) )>(fMin) ) pop( sortIndex( b(j) ), : )=bestX+(randn(1,dim)).*(abs(( pop( sortIndex( b(j) ), : ) -bestX))); else pop( sortIndex( b(j) ), : ) =pop( sortIndex( b(j) ), : )+(2*rand(1)-1)*(abs(pop( sortIndex( b(j) ), : )-worse))/ ( pFit( sortIndex( b(j) ) )-fmax+1e-50); end fitness(sortIndex( i ))=fun(pop(sortIndex( i ),:),inputnum,hiddennum,outputnum,net,inputn,outputn); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : sizepop if ( fitness( i ) < pFit( i ) ) pFit( i ) = fitness( i ); pop(i,:) = pop(i,:); end if( pFit( i ) < fMin ) fMin= pFit( i ); bestX =pop( i, : ); end end yy(t)=fMin; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 迭代寻优 x=bestX %% 结果分析 plot(yy) title(['适应度曲线 ' '终止代数＝' num2str(maxgen)]); xlabel('进化代数');ylabel('适应度'); %% 把最优初始阀值权值赋予网络预测 % %用麻雀搜索算法优化的BP网络进行值预测 w1=x(1:inputnum*hiddennum); B1=x(inputnum*hiddennum+1:inputnum*hiddennum+hiddennum); w2=x(inputnum*hiddennum+hiddennum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum); B2=x(inputnum*hiddennum+hiddennum+hiddennum*outputnum+1:inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum); net.iw{1,1}=reshape(w1,hiddennum,inputnum); net.lw{2,1}=reshape(w2,outputnum,hiddennum); net.b{1}=reshape(B1,hiddennum,1); net.b{2}=B2; %% 训练 %网络进化参数 net.trainParam.epochs=100; net.trainParam.lr=0.1; net.trainParam.goal=0.00001; %网络训练 [net,tr]=train(net,inputn,outputn); %%预测 %数据归一化 inputn_test=mapminmax('apply',input_test,inputps); an=sim(net,inputn_test); test_simu=mapminmax('reverse',an,outputps); error=test_simu-output_test; figure(2) plot(error) title('仿真预测误差','fontsize',12); xlabel('仿真次数','fontsize',12);ylabel('误差百分值','fontsize',12);