www.pudn.com > ctrl1.rar > CHAP5_4.M, change:2002-10-16,size:2847b


%chap5_4.m 
%GA(Generic Algorithm) Program is to predict Parameters of Friction 
clear all; 
close all; 
global rin yout timef F 
 
Size=30; 
 
F=2; 
if F==1 
   CodeL=1; 
   MinX=zeros(CodeL,1); 
   MaxX=1.0*ones(CodeL,1); 
end 
if F==2 
   CodeL=2; 
   MinX=zeros(CodeL,1); 
   MaxX=2.0*ones(CodeL,1); 
end 
 
for i=1:1:CodeL 
    kxi(:,i)=MinX(i)+(MaxX(i)-MinX(i))*rand(Size,1); 
end 
 
G=50; 
BsJ=0; 
 
for kg=1:1:G 
    time(kg)=kg; 
 
%****** Step 1:Evaluate BestJ ****** 
for i=1:1:Size 
    kx=kxi(i,:); 
     
[kx,BsJ]=chap5_4f(kx,BsJ); 
 
BsJi(i)=BsJ;    
end 
  
[OderJi,IndexJi]=sort(BsJi); 
BestJ(kg)=OderJi(1); 
BJ=BestJ(kg); 
Ji=BsJi+1e-10; 
 
   fi=1./Ji; 
%  Cm=max(Ji); 
%  fi=Cm-Ji;                         %Avoiding deviding zero 
    
   [Oderfi,Indexfi]=sort(fi);        %Arranging fi small to bigger 
   Bestfi=Oderfi(Size);              %Let Bestfi=max(fi) 
   BestS=kxi(Indexfi(Size),:);       %Let BestS=E(m), m is the Indexfi belong to max(fi) 
    
   kg    
   BJ 
   BestS 
   kx 
%****** 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 
   r=Size-sum(fi_S); 
    
   Rest=fi_Size-fi_S; 
   [RestValue,Index]=sort(Rest); 
    
   for i=Size:-1:Size-r+1 
       fi_S(Index(i))=fi_S(Index(i))+1; %Adding rest to equal Size 
   end 
 
   k=1; 
   for i=Size:-1:1                      %Select the Sizeth and Reproduce first!   
      for j=1:1:fi_S(i)                 %Notice: If i=1:1:Size then k plus meaningless 
       TempE(k,:)=kxi(Indexfi(i),:);    %Select and Reproduce  
         k=k+1;                         %k is used to reproduce 
      end 
   end 
    
%************ Step 3 : Crossover Operation ************ 
    Pc=0.90; 
    for i=1:2:(Size-1) 
          temp=rand; 
      if Pc>temp                         %Crossover Condition 
          alfa=rand; 
          TempE(i,:)=alfa*kxi(i+1,:)+(1-alfa)*kxi(i,:);   
          TempE(i+1,:)=alfa*kxi(i,:)+(1-alfa)*kxi(i+1,:); 
      end 
    end 
    TempE(Size,:)=BestS; 
    kxi=TempE; 
     
%************ Step 4: Mutation Operation ************** 
Pm=0.10-[1:1:Size]*(0.01)/Size;          %Bigger fi, smaller Pm 
Pm_rand=rand(Size,CodeL); 
Mean=(MaxX + MinX)/2;  
Dif=(MaxX-MinX); 
 
   for i=1:1:Size 
      for j=1:1:CodeL 
         if Pm(i)>Pm_rand(i,j)           %Mutation Condition 
            TempE(i,j)=Mean(j)+Dif(j)*(rand-0.5); 
         end 
      end 
   end 
%Guarantee TempE(Size,:) belong to the best individual 
   TempE(Size,:)=BestS;  
   kxi=TempE; 
%*************************************************************** 
end 
Bestfi 
BestS 
Best_J=BestJ(G) 
  
figure(1); 
plot(timef,rin,'b',timef,yout,'r'); 
xlabel('Time(s)');ylabel('rin,yout'); 
figure(2); 
plot(time,BestJ,'r'); 
xlabel('Times');ylabel('Best J');