3a.m.txt

遗传算法优化RBF径向基神经网络的matlab程序源代码。

%GA(Generic Algorithm) to Optimize Initial Parameters of RBF Approaching 利用遗传算法优化RBF神经网络的结构，包括优化权值、高斯基的中心和宽度
clear all; 
close all; 
 
G=150; 
Size=30; 
CodeL=10; 
 
for i=1:1:3 
	MinX(i)=0.1*ones(1); 
	MaxX(i)=3*ones(1); 
end 
for i=4:1:9 
	MinX(i)=-3*ones(1); 
	MaxX(i)=3*ones(1); 
end 
for i=10:1:12 
	MinX(i)=-ones(1); 
	MaxX(i)=ones(1); 
end 
 
E=round(rand(Size,12*CodeL));    %Initial Code! 
 
BsJ=0; 
 
for kg=1:1:G 
time(kg)=kg; 
     
for s=1:1:Size 
m=E(s,:); 
 
for j=1:1:12 
y(j)=0; 
 
mj=m((j-1)*CodeL+1:1:j*CodeL); 
	for i=1:1:CodeL 
   	y(j)=y(j)+mj(i)*2^(i-1); 
	end 
		f(s,j)=(MaxX(j)-MinX(j))*y(j)/1023+MinX(j); 
end 
 
%****** Step 1 : Evaluate BestJ ****** 
p=f(s,:); 
     
[p,BsJ]=chap10_3b(p,BsJ); 
 
BsJi(s)=BsJ;    
end 
  
[OderJi,IndexJi]=sort(BsJi); 
BestJ(kg)=OderJi(1); 
BJ=BestJ(kg); 
Ji=BsJi+1e-10; 
 
fi=1./Ji; 
[Oderfi,Indexfi]=sort(fi);          %Arranging fi small to bigger 
Bestfi=Oderfi(Size);           % Let Bestfi=max(fi) 
BestS=E(Indexfi(Size),:);      % Let BestS=E(m), m is the Indexfi belong to max(fi) 
 
kg    
p 
BJ 
%****** Step 2 : Select and Reproduct Operation****** 
   fi_sum=sum(fi); 
   fi_Size=(Oderfi/fi_sum)*Size; 
    
   fi_S=floor(fi_Size);        %Selecting Bigger fi value 
    
   kk=1; 
   for i=1:1:Size 
      for j=1:1:fi_S(i)        %Select and Reproduce  
       TempE(kk,:)=E(Indexfi(i),:);   
         kk=kk+1;              %kk is used to reproduce 
      end 
   end 
    
%************ Step 3 : Crossover Operation ************ 
pc=0.60; 
n=ceil(20*rand); 
for i=1:2:(Size-1) 
    temp=rand; 
    if pc>temp                 %Crossover Condition 
    for j=n:1:20 
        TempE(i,j)=E(i+1,j); 
        TempE(i+1,j)=E(i,j); 
    end 
    end 
end 
TempE(Size,:)=BestS; 
E=TempE; 
    
%************ Step 4: Mutation Operation ************** 
pm=0.001-[1:1:Size]*(0.001)/Size; %Bigger fi, smaller pm 
   for i=1:1:Size 
      for j=1:1:12*CodeL 
         temp=rand; 
         if pm>temp               %Mutation Condition 
            if TempE(i,j)==0 
               TempE(i,j)=1; 
            else 
               TempE(i,j)=0; 
            end 
        end 
      end 
   end 
%Guarantee TempE(Size,:) belong to the best individual 
TempE(Size,:)=BestS; 
E=TempE; 
%******************************************************* 
 end 
  
 Bestfi 
 BestS 
 fi 
 Best_J=BestJ(G) 
 figure(1); 
 plot(time,BestJ); 
 xlabel('Times');ylabel('Best J'); 
 save pfile p;