回归预测 | Matlab实现MPA-BP海洋捕食者算法优化BP神经网络多变量回归预测

效果一览

基本介绍

1.Matlab实现MPA-BP海洋捕食者算法优化BP神经网络多变量回归预测；
2.数据集为excel，输入7个特征，输出1个变量，运行主程序main.m即可，其余为函数文件，无需运行；
3.优化参数为神经网络的权值和偏置，命令窗口输出RMSE、MAPE、MAE、R2等评价指标；
4.运行环境Matlab2018b及以上。

程序设计

• 完整程序和数据获取方式：私信博主回复Matlab实现MPA-BP海洋捕食者算法优化BP神经网络多变量回归预测；

function [Top_predator_fit,Top_predator_pos,Convergence_curve]=MPA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)


Top_predator_pos=zeros(1,dim);
Top_predator_fit=inf; 

Convergence_curve=zeros(1,Max_iter);
stepsize=zeros(SearchAgents_no,dim);
fitness=inf(SearchAgents_no,1);


Prey=initialization(SearchAgents_no,dim,ub,lb);
  
Xmin=repmat(ones(1,dim).*lb,SearchAgents_no,1);
Xmax=repmat(ones(1,dim).*ub,SearchAgents_no,1);
         

Iter=0;
FADs=0.2;
P=0.5;

while Iter<Max_iter  
    
     %------------------- Detecting top predator -----------------    
 for i=1:size(Prey,1)  
        
    Flag4ub=Prey(i,:)>ub;
    Flag4lb=Prey(i,:)<lb;    
    Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                    
        
    fitness(i,1)=fobj(Prey(i,:));
                     
     if fitness(i,1)<Top_predator_fit 
       Top_predator_fit=fitness(i,1); 
       Top_predator_pos=Prey(i,:);
     end          
 end
     
     %------------------- Marine Memory saving ------------------- 
    
 if Iter==0
   fit_old=fitness;    Prey_old=Prey;
 end
     
  Inx=(fit_old<fitness);
  Indx=repmat(Inx,1,dim);
  Prey=Indx.*Prey_old+~Indx.*Prey;
  fitness=Inx.*fit_old+~Inx.*fitness;
        
  fit_old=fitness;    Prey_old=Prey;

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229