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)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) 
        参考资料
         
         
         
        [1] https://blog.csdn.net/kjm13182345320/article/details/129215161
        [2] https://blog.csdn.net/kjm13182345320/article/details/128105718
         
         
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