基于麻雀算法优化BP神经网络的多输入单输出预测（代码完整，数据齐全）

% 清空环境 clc clear close all warning off %读取数据 [num0 ,ax0,ay0]= xlsread('ssa-bp数据1.xlsx'); [mx,my] = size(ay0); for ii = 2:mx for jj = 1:my if ischar(ay0{ii,jj}) num0(ii-1,jj) = str2double(ay0{ii,jj}); end end end num0(:,1) = []; [num ,ax,ay]= xlsread('预测数据.xlsx'); [mx,my] = size(ay); for ii = 2:mx for jj = 1:my if ischar(ay{ii,jj}) num(ii-1,jj) = str2double(ay{ii,jj}); end end end num(:,1) = []; m1=18; %节点个数 inputnum=18; hiddennum=20; outputnum=1; m = round(0.8*length(num)); n = randperm(length(num)); %训练数据和预测数据 input_train=num0(n(1:m),1:m1)';% input_test=num0(m+1:end,1:m1)'; output_train=num0(n(1:m),m1+1)';% output_test=num0(m+1:end,m1+1)'; input_yu=num(:,1:m1)'; output_yu=num(:,m1+1)'; %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); net = newff(minmax(inputn),[20,1],{'logsig','tansig'},'traingdx'); [BPoutput,errorb] = bpp(input_train,input_test,output_train,output_test); % 参数初始化 dim=inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum; maxgen=30; % 进化次数 sizepop=22; %种群规模 popmax=5; popmin=-5; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% P_percent = 0.35; % 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,:)=10*rands(1,dim)-5; % 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; net = newff(minmax(inputn),[20,1],{'logsig','tansig'},'trainlm'); %% 训练 %网络进化参数 net.trainParam.epochs=5000; net.trainParam.lr=0.01; net.trainParam.goal=0.001; net.trainParam.max_fail = 3000; %网络训练 [net,tr]=train(net,inputn,outputn); load maynet.mat %%预测 %数据归一化 inputn_test=mapminmax('apply',input_test,inputps); inputn_yu=mapminmax('apply',input_yu,inputps); anyu=sim(net,inputn_yu); an=sim(net,inputn_test); test_simu=mapminmax('reverse',an,outputps); error=test_simu-output_test; test_simuyu=mapminmax('reverse',anyu,outputps); figure plot(test_simu,'r-o') hold on plot(BPoutput,'b-s') hold on plot(output_test,'k-*') hold off xlabel('样本') ylabel('价值') legend('ssa-bp','bp','实际值') figure plot(error) title('仿真预测误差','fontsize',12); xlabel('仿真次数','fontsize',12); ylabel('误差','fontsize',12); xlswrite('ssa-bp测试.xlsx',test_simu) xlswrite('bp测试.xlsx',BPoutput) xlswrite('ssa-bp测试.xlsx',test_simu) xlswrite('ssa-bp预测.xlsx',test_simuyu)