回归预测 | Matlab实现DE-BP差分算法优化BP神经网络多变量回归预测
目录
回归预测 | Matlab实现DE-BP差分算法优化BP神经网络多变量回归预测效果一览基本介绍程序设计参考资料
效果一览
基本介绍
1.Matlab实现DE-BP差分算法优化BP神经网络多变量回归预测（完整源码和数据）
2.运行环境为Matlab2018b；
3.excel数据集，输入多个特征，输出单个变量，多变量回归预测预测，DE_BP.m为主程序，运行即可,所有文件放在一个文件夹；
4.输出优化前后对比图，误差对比图；
代码特点：参数化编程、参数可方便更改、代码编程思路清晰、注释明细。
程序设计
完整源码和数据获取方式(资源处下载)：Matlab实现DE-BP差分算法优化BP神经网络多变量回归预测。%% 清空环境变量%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);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%优化前的BPnet.trainParam.epochs=100;net.trainParam.lr=0.1;net.trainParam.goal=0.00001;[net,~]=train(net,inputn,outputn);inputn_test=mapminmax('apply',input_test,inputps);an=sim(net,inputn_test);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 利用差分进化算法选择最佳的BP参数D=inputnum*hiddennum+hiddennum+hiddennum*outputnum+outputnum;%变量个数%变量的维数NP=5; %个体数目 G=30; %最大进化代数%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Xs=1*ones(D,1); %上限Xx=-1*ones(D,1); %下限%%%%%%%%%%%%%%%%%%%%%%%%%赋初值%%%%%%%%%%%%%%%%%%%%%%%% xx=zeros(D,NP); %初始种群v=zeros(D,NP); %变异种群u=zeros(D,NP); %选择种群xchu=rand(D,NP);for i=1:NPxx(:,i)=xchu(:,i).*(Xs-Xx)+Xx; %赋初始种群初值end%%%%%%%%%%%%%%%%%%%%计算目标函数%%%%%%%%%%%%%%%%%%%%%%%trace(1)=min(Ob);gbest=100;%%%%%%%%%%%%%%%%%%%%%%%差分进化循环%%%%%%%%%%%%%%%%%%%%%for gen=1:G %%%%%%%%%%%%%%%%%%%%%%变异操作%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%r1,r2,r3和m互不相同%%%%%%%%%%%%%%% for m=1:NP r1=randi([1,NP],1,1); while (r1==m) r1=randi([1,NP],1,1); end r2=randi([1,NP],1,1); while (r2==m)||(r2==r1) r2=randi([1,NP],1,1); end r3=randi([1,NP],1,1); while (r3==m)||(r3==r1)||(r3==r2) r3=randi([1,NP],1,1); end v(:,m)=xx(:,r1)+F*(xx(:,r2)-xx(:,r3)); end %%%%%%%%%%%%%%%%%%%%%%交叉操作%%%%%%%%%%%%%%%%%%%%%%% r=randi([1,D],1,1); for n=1:D cr=rand(1); if (cr<=CR)||(n==r) u(n,:)=v(n,:); else u(n,:)=xx(n,:); end end %%%%%%%%%%%%%%%%%%%边界条件的处理%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%边界吸收%%%%%%%%%%%%%%%%%%%%%%%%% for n=1:D for m=1:NP if u(n,m)<Xx(n) u(n,m)=Xx(n); end if u(n,m)>Xs(n) u(n,m)=Xs(n); end end end %%%%%%%%%%%%%%%%%%%%%%选择操作%%%%%%%%%%%%%%%%%%%%%%% for m=1:NP Ob1(m)=fitness(u(:,m)); end for m=1:NP if Ob1(m)<Ob(m) xx(:,m)=u(:,m); end end for m=1:NP Ob(m)=fitness(xx(:,m)); end fbest=min(Ob); if(fbest<gbest) gbest=fbest; histor(gen)=fbest; else histor(gen)=gbest; endend参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718