开源代码！|66种智能优化算法和改进优化算法优化BP神经网络

function fitness_value =objfun(input_pop)global input_num hidden_num output_num input_data output_data    w1=input_pop(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数    B1=input_pop(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置    w2=input_pop(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...        hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置    B2=input_pop(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...        hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置    %网络权值赋值    W1=reshape(w1,hidden_num,input_num);    W2=reshape(w2,output_num,hidden_num);    B1=reshape(B1,hidden_num,1);    B2=reshape(B2,output_num,1);    [~,n]=size(input_data);    A1=logsig(W1*input_data+repmat(B1,1,n));   %需与main函数中激活函数相同    A2=purelin(W2*A1+repmat(B2,1,n));      %需与main函数中激活函数相同      error=sumsqr(output_data-A2);    fitness_value=error; %误差即为适应度end

clc;clear; close all;load('abalone_data.mat')%鲍鱼数据global input_num hidden_num output_num input_data output_data train_num test_num  x_train_mu y_train_mu x_train_sigma  y_train_sigma%% 导入数据%设置训练数据和测试数据[m,n]=size(data);train_num=round(0.8*m); %自变量 test_num=m-train_num;x_train_data=data(1:train_num,1:n-1);y_train_data=data(1:train_num,n);%测试数据x_test_data=data(train_num+1:end,1:n-1);y_test_data=data(train_num+1:end,n);
%% 标准化[x_train_regular,x_train_mu,x_train_sigma] = zscore(x_train_data);[y_train_regular,y_train_mu,y_train_sigma]= zscore(y_train_data);x_train_regular=x_train_regular';y_train_regular=y_train_regular';input_data=x_train_regular;output_data=y_train_regular;input_num=size(x_train_regular,1); %输入特征个数hidden_num=6;   %隐藏层神经元个数output_num=size(y_train_regular,1); %输出特征个数
num_all=input_num*hidden_num+hidden_num+hidden_num*output_num+output_num;%网络总参数，只含一层隐藏层;%%%自变量的个数即为网络连接权重以及偏置popmax=1.5;   %自变量即网络权重和偏置的上限popmin=-1.5;  %自变量即网络权重和偏置的下限SearchAgents_no=50; % Number of search agents  搜索麻雀数量Max_iteration=300; % Maximum numbef of iterations 最大迭代次数% Load details of the selected benchmark function

%%fobj=@objfun;Time_compare=[];      %算法的运行时间比较Fival_compare=[];       %算法的最终目标比较Fival_compare1=[];     %优化过后的初始参数经过反向传播的优化Fival_compare2=[];curve_compare=[];     %算法的过程函数比较name_all=[];     %算法的名称记录dim=num_all;lb=popmin;ub=popmax;pop_num=SearchAgents_no;Max_iter=Max_iteration;%% 不进行优化，随机赋值iter=1;bestX=lb+(ub-lb).*rand(1,num_all);ER_=Fun1(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_1=Fun2(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);Fival_compare1=[Fival_compare1,ER_]; Fival_compare2=[Fival_compare2,ER_1]; name_all{1,iter}='NO-opti';iter=iter+1;%% 麻雀搜索算法t1=clock;[fMin_SSA,bestX_SSA,SSA_curve]=SSA(pop_num,Max_iter,lb,ub,dim,fobj);    %麻雀搜索算法
ER_SSA=Fun1(bestX_SSA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_SSA1=Fun2(bestX_SSA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);t2=clock;time_SSA=(t2(end)+t2(end-1)*60+t2(end-2)*3600-t1(end)-t1(end-1)*60-t1(end-2)*3600);Fival_compare=[Fival_compare,fMin_SSA];   Fival_compare1=[Fival_compare1,ER_SSA]; Fival_compare2=[Fival_compare2,ER_SSA1]; Time_compare=[Time_compare,time_SSA(end)];curve_compare=[curve_compare;SSA_curve];name_all{1,iter}='SSA';iter=iter+1;

function [EcRMSE]=Fun1(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data)global input_num output_num x_train_mu y_train_mu x_train_sigma  y_train_sigmabestchrom=bestX;net=newff(x_train_regular,y_train_regular,hidden_num,{'logsig','purelin'},'trainlm');w1=bestchrom(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数B1=bestchrom(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置w2=bestchrom(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置B2=bestchrom(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置%网络权值赋值net.iw{1,1}=reshape(w1,hidden_num,input_num);net.lw{2,1}=reshape(w2,output_num,hidden_num);net.b{1}=reshape(B1,hidden_num,1);net.b{2}=reshape(B2,output_num,1);net.trainParam.epochs=200;          %最大迭代次数net.trainParam.lr=0.1;                        %学习率net.trainParam.goal=0.00001;[net,~]=train(net,x_train_regular,y_train_regular);%将输入数据归一化test_num=size(x_test_data,1);x_test_regular = (x_test_data-repmat(x_train_mu,test_num,1))./repmat(x_train_sigma,test_num,1);%放入到网络输出数据y_test_regular=sim(net,x_test_regular');%将得到的数据反归一化得到预测数据 test_out_std=y_test_regular;%反归一化SSA_BP_predict=test_out_std*y_train_sigma+y_train_mu;errors_nn=sum(abs(SSA_BP_predict'-y_test_data)./(y_test_data))/length(y_test_data);EcRMSE=sqrt(sum((errors_nn).^2)/length(errors_nn));% disp(EcRMSE)end

function [EcRMSE]=Fun2(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data)global input_num output_num  x_train_mu y_train_mu x_train_sigma  y_train_sigma
train_num=length(y_train_regular); %自变量 test_num=length(y_test_data);bestchrom=bestX;% net=newff(x_train_regular,y_train_regular,hidden_num,{'tansig','purelin'},'trainlm');w1=bestchrom(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数B1=bestchrom(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置w2=bestchrom(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置B2=bestchrom(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置%网络权值赋值x_train_std=x_train_regular;y_train_std=y_train_regular;%vij = reshape(w1,hidden_num,input_num) ;%输入和隐藏层的权重theta_u = reshape(B1,hidden_num,1);%输入与隐藏层之间的阈值Wj =  reshape(w2,output_num,hidden_num);%%输出和隐藏层的权重theta_y =reshape(B2,output_num,1);%输出与隐藏层之间的阈值%learn_rate = 0.0001;%学习率Epochs_max = 10000;%最大迭代次数error_rate = 0.1;%目标误差Obj_save = zeros(1,Epochs_max);%损失函数% 训练网络epoch_num=0;while epoch_num <Epochs_max    epoch_num=epoch_num+1;      y_pre_std_u=vij * x_train_std + repmat(theta_u, 1, train_num);    y_pre_std_u1 = logsig(y_pre_std_u);      y_pre_std_y = Wj * y_pre_std_u1 + repmat(theta_y, 1, train_num);      y_pre_std_y1=y_pre_std_y;    obj =  y_pre_std_y1-y_train_std ;        Ems = sumsqr(obj);    Obj_save(epoch_num) = Ems;     if Ems < error_rate        break;    end     %梯度下降    %输出采用rule函数，隐藏层采用sigomd激活函数    c_wj= 2*(y_pre_std_y1-y_train_std)* y_pre_std_u1';        c_theta_y=2*(y_pre_std_y1-y_train_std)*ones(train_num, 1);        c_vij=Wj'* 2*(y_pre_std_y1-y_train_std).*(y_pre_std_u1).*(1-y_pre_std_u1)* x_train_std';        c_theta_u=Wj'* 2*(y_pre_std_y1-y_train_std).*(y_pre_std_u1).*(1-y_pre_std_u1)* ones(train_num, 1);        Wj=Wj-learn_rate*c_wj;        theta_y=theta_y-learn_rate*c_theta_y;        vij=vij- learn_rate*c_vij;         theta_u=theta_u-learn_rate*c_theta_u;end % x_test_regular = (x_test_data-repmat(x_train_mu,test_num,1))./repmat(x_train_sigma,test_num,1);% x_test_regular = mapminmax('apply',x_test_data,x_train_maxmin);%放入到网络输出数据x_test_std=x_test_regular';test_put = logsig(vij * x_test_std + repmat(theta_u, 1, test_num));test_out_std =  Wj * test_put + repmat(theta_y, 1, test_num);%反归一化SSA_BP_predict=test_out_std*y_train_sigma+y_train_mu;errors_nn=sum(abs(SSA_BP_predict'-y_test_data)./(y_test_data))/length(y_test_data);EcRMSE=sqrt(sum((errors_nn).^2)/length(errors_nn));disp(EcRMSE)end

clc;clear; close all;load('data_test1.mat')global input_num hidden_num output_num input_data output_data train_num test_num  x_train_mu y_train_mu x_train_sigma  y_train_sigma%%  循环5次记录结果  训练集和测试集随机%% 导入数据  %设置训练数据和测试数据
NUM=5;    %随机测试数for  NN=1:NUM[m,n]=size(data);train_num=round(0.8*m); %自变量 randlabel=randperm(m);   %随机标签test_num=m-train_num;x_train_data=data(randlabel(1:train_num),1:n-1);y_train_data=data(randlabel(1:train_num),n);%测试数据x_test_data=data(randlabel(train_num+1:end),1:n-1);y_test_data=data(randlabel(train_num+1:end),n);% x_train_data=x_train_data';% y_train_data=y_train_data';% x_test_data=x_test_data';%% 标准化[x_train_regular,x_train_mu,x_train_sigma] = zscore(x_train_data);[y_train_regular,y_train_mu,y_train_sigma]= zscore(y_train_data);x_train_regular=x_train_regular';y_train_regular=y_train_regular';input_data=x_train_regular;output_data=y_train_regular;input_num=size(x_train_regular,1); %输入特征个数hidden_num=6;   %隐藏层神经元个数output_num=size(y_train_regular,1); %输出特征个数num_all=input_num*hidden_num+hidden_num+hidden_num*output_num+output_num;%网络总参数，只含一层隐藏层;%自变量的个数即为网络连接权重以及偏置popmax=1.5;   %自变量即网络权重和偏置的上限popmin=-1.5;  %自变量即网络权重和偏置的下限SearchAgents_no=50; % Number of search agents  搜索麻雀数量Max_iteration=300; % Maximum numbef of iterations 最大迭代次数% Load details of the selected benchmark function%%fobj=@objfun;Time_compare=[];      %算法的运行时间比较Fival_compare=[];       %算法的最终目标比较Fival_compare1=[];     %优化过后的初始参数经过反向传播的优化Fival_compare2=[];curve_compare=[];     %算法的过程函数比较name_all=[];     %算法的名称记录dim=num_all;lb=popmax*ones(1,dim);ub=popmin*ones(1,dim);pop_num=SearchAgents_no;Max_iter=Max_iteration;%% 不进行优化，随机赋值iter=1;bestX=lb+(ub-lb).*rand(1,num_all);ER_=Fun1(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_1=Fun2(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);Fival_compare1=[Fival_compare1,ER_]; Fival_compare2=[Fival_compare2,ER_1]; name_all{1,iter}='ON-opti';iter=iter+1;%%% [ER_1,WW]=Fun2(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);% % [fMin_SSA,bestX_SSA,SSA_curve]=SSA2(pop_num,pop_or,Max_iter,lb,ub,dim,fobj); % % ER2=fun3(bestX_SSA,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data);% pop_num11=500;% [fMin_SSA,bestX_SSA1,SSA_curve]=SSA2(pop_num11,WW,Max_iter,lb,ub,dim,fobj); % ER_2=fun3(bestX_SSA1,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data);% Fival_compare1=[Fival_compare1,ER_2]; % Fival_compare2=[Fival_compare2,ER_2]; % name_all{1,iter}='BP-SSA';% iter=iter+1;%% 改进麻雀搜索算法t1=clock;[fMin_SSA,bestX_SSA,SSA_curve]=G_SSA(pop_num,Max_iter,lb,ub,dim,fobj);    %麻雀搜索算法ER_SSA=Fun1(bestX_SSA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_SSA1=Fun2(bestX_SSA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);t2=clock;time_SSA=(t2(end)+t2(end-1)*60+t2(end-2)*3600-t1(end)-t1(end-1)*60-t1(end-2)*3600);ER_SSA2=fun3(bestX_SSA,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data); %不进行BP反向Fival_compare=[Fival_compare,ER_SSA2];   Fival_compare1=[Fival_compare1,ER_SSA]; Fival_compare2=[Fival_compare2,ER_SSA1]; Time_compare=[Time_compare,time_SSA(end)];curve_compare=[curve_compare;SSA_curve];name_all{1,iter}='G-SSA';iter=iter+1;%%%改进鲸鱼优化算法t1=clock;[fMin_EWOA,bestX_EWOA,EWOA_curve]=BKA(pop_num,Max_iter,lb,ub,dim,fobj); ER_EWOA=Fun1(bestX_EWOA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_EWOA1=Fun2(bestX_EWOA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);t2=clock;time_EWOA=(t2(end)+t2(end-1)*60+t2(end-2)*3600-t1(end)-t1(end-1)*60-t1(end-2)*3600);ER_EWOA2=fun3(bestX_EWOA,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data); %不进行BP反向Fival_compare=[Fival_compare,ER_EWOA2];Fival_compare1=[Fival_compare1,ER_EWOA]; Fival_compare2=[Fival_compare2,ER_EWOA1]; Time_compare=[Time_compare,time_EWOA(end)];curve_compare=[curve_compare;EWOA_curve];name_all{1,iter}='BKA';iter=iter+1;%%%正弦余弦优化算法 Sine Cosine Algorithmt1=clock;[fMin_SCA,bestX_SCA,SCA_curve]=SCA(pop_num,Max_iter,lb,ub,dim,fobj); ER_SCA=Fun1(bestX_SCA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_SCA1=Fun2(bestX_SCA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);t2=clock;time_SCA=(t2(end)+t2(end-1)*60+t2(end-2)*3600-t1(end)-t1(end-1)*60-t1(end-2)*3600);ER_SCA2=fun3(bestX_EWOA,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data); %不进行BP反向Fival_compare=[Fival_compare,ER_SCA2];Fival_compare1=[Fival_compare1,ER_SCA]; Fival_compare2=[Fival_compare2,ER_SCA1]; Time_compare=[Time_compare,time_SCA(end)];curve_compare=[curve_compare;SCA_curve];name_all{1,iter}='SCA';iter=iter+1;%% %POA  %IGOA%IGWOt1=clock;[fMin_SCA,bestX_SCA,SCA_curve]=G_DBO(pop_num,Max_iter,lb,ub,dim,fobj); ER_SCA=Fun1(bestX_SCA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);ER_SCA1=Fun2(bestX_SCA,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data);t2=clock;ER_SCA2=fun3(bestX_EWOA,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data); %不进行BP反向time_SCA=(t2(end)+t2(end-1)*60+t2(end-2)*3600-t1(end)-t1(end-1)*60-t1(end-2)*3600);Fival_compare=[Fival_compare,ER_SCA2];Fival_compare1=[Fival_compare1,ER_SCA]; Fival_compare2=[Fival_compare2,ER_SCA1]; Time_compare=[Time_compare,time_SCA(end)];curve_compare=[curve_compare;SCA_curve];name_all{1,iter}='G-DBO';iter=iter+1;FFival_compare1(NN,:)=Fival_compare1;FFival_compare2(NN,:)=Fival_compare2;end%%load('color_list.mat')figure(3)color=color_list(randperm(length(color_list)),:);width=0.7; %柱状图宽度for  i=1:length(Fival_compare1)    set(bar(i,Fival_compare1(i),width),'FaceColor',color(i,:),'EdgeColor',[0,0,0],'LineWidth',2)   hold on       %在柱状图 x,y 基础上 绘制误差 ,low为下误差，high为上误差，LineStyle 误差图样式，'Color' 误差图颜色     % 'LineWidth', 线宽,'CapSize',误差标注大小%    errorbar(i, y(i), low(i), high(i), 'LineStyle', 'none', 'Color', color(i+3,:), 'LineWidth', 1.5,'CapSize',18);endylabel('obj-value')ylim([min(Fival_compare1)-0.01,max(Fival_compare1)+0.01]);ax=gca;ax.XTick = 1:1:length(Fival_compare1);set(gca,'XTickLabel',name_all,"LineWidth",2);set(gca,"FontSize",12,"LineWidth",2)title('优化工具箱')%%load('color_list.mat')figure(4)color=color_list(randperm(length(color_list)),:);width=0.7; %柱状图宽度for  i=1:length(Fival_compare2)    set(bar(i,Fival_compare2(i),width),'FaceColor',color(i,:),'EdgeColor',[0,0,0],'LineWidth',2)   hold on       %在柱状图 x,y 基础上 绘制误差 ,low为下误差，high为上误差，LineStyle 误差图样式，'Color' 误差图颜色     % 'LineWidth', 线宽,'CapSize',误差标注大小%    errorbar(i, y(i), low(i), high(i), 'LineStyle', 'none', 'Color', color(i+3,:), 'LineWidth', 1.5,'CapSize',18);endylabel('obj-value')ylim([min(Fival_compare2)-0.01,max(Fival_compare2)+0.01]);ax=gca;ax.XTick = 1:1:length(Fival_compare2);set(gca,'XTickLabel',name_all,"LineWidth",2);set(gca,"FontSize",12,"LineWidth",2)title('自写网络')%%figure(5)bar([Fival_compare1;Fival_compare2]')ylabel('obj-value')ylim([min(Fival_compare2)-0.01,max(Fival_compare2)+0.01]);ax=gca;ax.XTick = 1:1:length(Fival_compare2);set(gca,'XTickLabel',name_all,"LineWidth",2);set(gca,"FontSize",12,"LineWidth",2)legend('工具箱','自写网络')%%figure(7)color=[0.741176470588235,0.729411764705882,0.725490196078431;0.525490196078431,...    0.623529411764706,0.752941176470588;0.631372549019608,0.803921568627451,...    0.835294117647059;0.588235294117647,0.576470588235294,0.576470588235294;...    0.0745098039215686,0.407843137254902,0.607843137254902;0.454901960784314,...    0.737254901960784,0.776470588235294;0.0156862745098039,0.0196078431372549,0.0156862745098039];% 颜色1mean_compare1=mean(FFival_compare1);std1_compare1=std(FFival_compare1);mean_compare2=mean(FFival_compare2);std1_compare2=std(FFival_compare2);b=bar([mean_compare1;mean_compare2]');data=[mean_compare1;mean_compare2]';hold onerro_data=[std1_compare1;std1_compare1]';ax = gca;for i = 1 : 2    x_data(:, i) = b(i).XEndPoints'; end
for i=1:2errorbar(x_data(:,i),data(:,i),erro_data(:,i),'LineStyle', 'none','Color',color(i+3,:) ,'LineWidth', 2,'CapSize',18)end
for i =1:2b(i).FaceColor = color(i,:);b(i).EdgeColor= color(i+3,:);b(i).LineWidth=1.5;endylabel('obj-value')maxl=max([mean_compare1,mean_compare2]);minl=min([mean_compare1,mean_compare2]);ylim([minl-0.01,maxl+0.01]);ax=gca;ax.XTick = 1:1:length(Fival_compare2);set(gca,'XTickLabel',name_all,"LineWidth",2);set(gca,"FontSize",12,"LineWidth",2)legend('工具箱','自写网络')box off% net=newff(x_train_regular,y_train_regular,hidden_num,{'logsig','purelin'},'trainlm','deviderand');%%% function fitness_value =objfun(input_pop,input_num,hidden_num,output_num,input_data,output_data)function fitness_value =objfun(input_pop)global input_num hidden_num output_num input_data output_data    w1=input_pop(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数    B1=input_pop(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置    w2=input_pop(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...        hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置    B2=input_pop(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...        hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置    %网络权值赋值    W1=reshape(w1,hidden_num,input_num);    W2=reshape(w2,output_num,hidden_num);    B1=reshape(B1,hidden_num,1);    B2=reshape(B2,output_num,1);    [~,n]=size(input_data);    A1=logsig(W1*input_data+repmat(B1,1,n));   %需与main函数中激活函数相同    A2=purelin(W2*A1+repmat(B2,1,n));      %需与main函数中激活函数相同      error=sumsqr(output_data-A2);    fitness_value=error; %误差即为适应度end%%function EcRMSE =fun3(input_pop,x_test_data,x_train_mu,x_train_sigma,y_train_mu,y_train_sigma,y_test_data)global input_num hidden_num output_num        w1=input_pop(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数    B1=input_pop(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置    w2=input_pop(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...        hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置    B2=input_pop(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...        hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置    %网络权值赋值%     W1=reshape(w1,hidden_num,input_num);%     W2=reshape(w2,output_num,hidden_num);%     B1=reshape(B1,hidden_num,1);%     B2=reshape(B2,output_num,1);    vij = reshape(w1,hidden_num,input_num) ;%输入和隐藏层的权重theta_u = reshape(B1,hidden_num,1);%输入与隐藏层之间的阈值Wj =  reshape(w2,output_num,hidden_num);%%输出和隐藏层的权重theta_y =reshape(B2,output_num,1);%输出与隐藏层之间的阈值%     [~,n]=size(input_data);%     A1=logsig(W1*input_data+repmat(B1,1,n));   %需与main函数中激活函数相同%     A2=purelin(W2*A1+repmat(B2,1,n));      %需与main函数中激活函数相同  %     error=sumsqr(output_data-A2);%     fitness_value=error; %误差即为适应度test_num=size(x_test_data,1);     x_test_regular = (x_test_data-repmat(x_train_mu,test_num,1))./repmat(x_train_sigma,test_num,1);% x_test_regular = mapminmax('apply',x_test_data,x_train_maxmin);%放入到网络输出数据x_test_std=x_test_regular';test_put = logsig(vij * x_test_std + repmat(theta_u, 1, test_num));test_out_std =  Wj * test_put + repmat(theta_y, 1, test_num);%反归一化SSA_BP_predict=test_out_std*y_train_sigma+y_train_mu;errors_nn=sum(abs(SSA_BP_predict'-y_test_data)./(y_test_data))/length(y_test_data);EcRMSE=sqrt(sum((errors_nn).^2)/length(errors_nn));% disp(EcRMSE)end%%function [EcRMSE,net1]=Fun1(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data)global input_num output_num x_train_mu y_train_mu x_train_sigma  y_train_sigmabestchrom=bestX;net=newff(x_train_regular,y_train_regular,hidden_num,{'logsig','purelin'},'trainlm');w1=bestchrom(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数B1=bestchrom(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置w2=bestchrom(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置B2=bestchrom(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置%网络权值赋值net.iw{1,1}=reshape(w1,hidden_num,input_num);net.lw{2,1}=reshape(w2,output_num,hidden_num);net.b{1}=reshape(B1,hidden_num,1);net.b{2}=reshape(B2,output_num,1);net.trainParam.epochs=200;          %最大迭代次数net.trainParam.lr=0.1;                        %学习率net.trainParam.goal=0.00001;[net,~]=train(net,x_train_regular,y_train_regular);%将输入数据归一化test_num=size(x_test_data,1);x_test_regular = (x_test_data-repmat(x_train_mu,test_num,1))./repmat(x_train_sigma,test_num,1);%放入到网络输出数据y_test_regular=sim(net,x_test_regular');net1=net;%将得到的数据反归一化得到预测数据 test_out_std=y_test_regular;%反归一化SSA_BP_predict=test_out_std*y_train_sigma+y_train_mu;errors_nn=sum(abs(SSA_BP_predict'-y_test_data)./(y_test_data))/length(y_test_data);EcRMSE=sqrt(sum((errors_nn).^2)/length(errors_nn));disp(EcRMSE)end%%function [EcRMSE,WW]=Fun2(bestX,x_train_regular,y_train_regular,hidden_num,x_test_data,y_test_data)global input_num output_num  x_train_mu y_train_mu x_train_sigma  y_train_sigma
train_num=length(y_train_regular); %自变量 test_num=length(y_test_data);bestchrom=bestX;% net=newff(x_train_regular,y_train_regular,hidden_num,{'tansig','purelin'},'trainlm');w1=bestchrom(1:input_num*hidden_num);   %输入和隐藏层之间的权重参数B1=bestchrom(input_num*hidden_num+1:input_num*hidden_num+hidden_num); %隐藏层神经元的偏置w2=bestchrom(input_num*hidden_num+hidden_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num);  %隐藏层和输出层之间的偏置B2=bestchrom(input_num*hidden_num+hidden_num+hidden_num*output_num+1:input_num*hidden_num+...    hidden_num+hidden_num*output_num+output_num); %输出层神经元的偏置%网络权值赋值x_train_std=x_train_regular;y_train_std=y_train_regular;%vij = reshape(w1,hidden_num,input_num) ;%输入和隐藏层的权重theta_u = reshape(B1,hidden_num,1);%输入与隐藏层之间的阈值Wj =  reshape(w2,output_num,hidden_num);%%输出和隐藏层的权重theta_y =reshape(B2,output_num,1);%输出与隐藏层之间的阈值%learn_rate = 0.0001;%学习率Epochs_max = 30000;%最大迭代次数error_rate = 0.001;%目标误差Obj_save = zeros(1,Epochs_max);%损失函数% 训练网络epoch_num=0;while epoch_num <Epochs_max    epoch_num=epoch_num+1;      y_pre_std_u=vij * x_train_std + repmat(theta_u, 1, train_num);    y_pre_std_u1 = logsig(y_pre_std_u);      y_pre_std_y = Wj * y_pre_std_u1 + repmat(theta_y, 1, train_num);      y_pre_std_y1=y_pre_std_y;    obj =  y_pre_std_y1-y_train_std ;        Ems = sumsqr(obj);    Obj_save(epoch_num) = Ems;     if Ems < error_rate        break;    end     %梯度下降    %输出采用rule函数，隐藏层采用sigomd激活函数    c_wj= 2*(y_pre_std_y1-y_train_std)* y_pre_std_u1';        c_theta_y=2*(y_pre_std_y1-y_train_std)*ones(train_num, 1);        c_vij=Wj'* 2*(y_pre_std_y1-y_train_std).*(y_pre_std_u1).*(1-y_pre_std_u1)* x_train_std';        c_theta_u=Wj'* 2*(y_pre_std_y1-y_train_std).*(y_pre_std_u1).*(1-y_pre_std_u1)* ones(train_num, 1);        Wj=Wj-learn_rate*c_wj;        theta_y=theta_y-learn_rate*c_theta_y;        vij=vij- learn_rate*c_vij;         theta_u=theta_u-learn_rate*c_theta_u;end WW=[vij(:);theta_u;Wj(:);theta_y];%  W1=[Wj(:),theta_y,vij(:),theta_u]; x_test_regular = (x_test_data-repmat(x_train_mu,test_num,1))./repmat(x_train_sigma,test_num,1);% x_test_regular = mapminmax('apply',x_test_data,x_train_maxmin);%放入到网络输出数据x_test_std=x_test_regular';test_put = logsig(vij * x_test_std + repmat(theta_u, 1, test_num));test_out_std =  Wj * test_put + repmat(theta_y, 1, test_num);%反归一化SSA_BP_predict=test_out_std*y_train_sigma+y_train_mu;errors_nn=sum(abs(SSA_BP_predict'-y_test_data)./(y_test_data))/length(y_test_data);EcRMSE=sqrt(sum((errors_nn).^2)/length(errors_nn));disp(EcRMSE)end