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
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。
clike
%% 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BP
net.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:NP
xx(:,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;
end
end参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718