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⛄ 内容介绍
介绍了用遗传算法优化模糊控制规则的方法,包括目标函数的确定,选择、交叉等遗传算子的确定.给出了如何用MATLAB编程实现算法的具体步骤,最后将所得的规则用于设计模糊控制器,并进行仿真,取得了很好的效果.
⛄ 部分代码
clear all;
close all;
global rin yout timef
G=50;
Size=30;
CodeL=10;
MinX(1)=zeros(1);
MaxX(1)=10*ones(1);
MinX(2)=zeros(1);
MaxX(2)=30*ones(1);
MinX(3)=zeros(1);
MaxX(3)=100*ones(1);
MinX(4)=-0.5*ones(1);
MaxX(4)=0.5*ones(1);
MinX(5)=-0.5*ones(1);
MaxX(5)=0.5*ones(1);
MinX(6)=-0.5*ones(1);
MaxX(6)=0.5*ones(1);
MinX(7)=-0.5*ones(1);
MaxX(7)=0.5*ones(1);
MinX(8)=-0.5*ones(1);
MaxX(8)=0.5*ones(1);
MinX(9)=-0.5*ones(1);
MaxX(9)=0.5*ones(1);
MinX(10)=-0.5*ones(1);
MaxX(10)=0.5*ones(1);
MinX(11)=-0.5*ones(1);
MaxX(11)=0.5*ones(1);
E=round(rand(Size,11*CodeL)); %Initial Code!
BsJ=0;
for kg=1:1:G
time(kg)=kg;
for s=1:1:Size
m=E(s,:);
y1=0;y2=0;y3=0;y4=0;y5=0;y5=0;y6=0;y7=0;y8=0;y9=0;y10=0;y11=0;y12=0;
m1=m(1:1:CodeL);
for i=1:1:CodeL
y1=y1+m1(i)*2^(i-1);
end
Kfuzzy(s,1)=(MaxX(1)-MinX(1))*y1/1023+MinX(1);
m2=m(CodeL+1:1:2*CodeL);
for i=1:1:CodeL
y2=y2+m2(i)*2^(i-1);
end
Kfuzzy(s,2)=(MaxX(2)-MinX(2))*y2/1023+MinX(2);
m3=m(2*CodeL+1:1:3*CodeL);
for i=1:1:CodeL
y3=y3+m3(i)*2^(i-1);
end
Kfuzzy(s,3)=(MaxX(3)-MinX(3))*y3/1023+MinX(3);
m4=m(3*CodeL+1:1:4*CodeL);
for i=1:1:CodeL
y4=y4+m4(i)*2^(i-1);
end
Kfuzzy(s,4)=(MaxX(4)-MinX(4))*y4/1023+MinX(4);
m5=m(4*CodeL+1:1:5*CodeL);
for i=1:1:CodeL
y5=y5+m5(i)*2^(i-1);
end
Kfuzzy(s,5)=(MaxX(5)-MinX(5))*y5/1023+MinX(5);
m6=m(5*CodeL+1:1:6*CodeL);
for i=1:1:CodeL
y6=y6+m6(i)*2^(i-1);
end
Kfuzzy(s,6)=(MaxX(6)-MinX(6))*y6/1023+MinX(6);
m7=m(6*CodeL+1:1:7*CodeL);
for i=1:1:CodeL
y7=y7+m7(i)*2^(i-1);
end
Kfuzzy(s,7)=(MaxX(7)-MinX(7))*y7/1023+MinX(7);
m8=m(7*CodeL+1:1:8*CodeL);
for i=1:1:CodeL
y8=y8+m8(i)*2^(i-1);
end
Kfuzzy(s,8)=(MaxX(8)-MinX(8))*y8/1023+MinX(8);
m9=m(8*CodeL+1:1:9*CodeL);
for i=1:1:CodeL
y9=y9+m9(i)*2^(i-1);
end
Kfuzzy(s,9)=(MaxX(9)-MinX(9))*y9/1023+MinX(9);
m10=m(9*CodeL+1:1:10*CodeL);
for i=1:1:CodeL
y10=y10+m10(i)*2^(i-1);
end
Kfuzzy(s,10)=(MaxX(10)-MinX(10))*y10/1023+MinX(10);
m11=m(10*CodeL+1:1:11*CodeL);
for i=1:1:CodeL
y11=y11+m11(i)*2^(i-1);
end
Kfuzzy(s,11)=(MaxX(11)-MinX(11))*y11/1023+MinX(11);
%****** Step 1 : Evaluate BestJ ******
Kfuzzyi=Kfuzzy(s,:);
[Kfuzzyi,BsJ]=mychap5_2f(Kfuzzyi,BsJ);
BsJi(s)=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=Kpid(Indexfi(Size),:); %Let BestS=E(m), m is the Indexfi belong to max(fi)
Bestfi=Oderfi(Size); % Let Bestfi=max(fi)
BestS=E(Indexfi(Size),:); % Let BestS=E(m), m is the Indexfi belong to max(fi)
kg
BJ
BestS;
%****** 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(100*rand);
for i=1:2:(Size-1)
temp=rand;
if pc>temp %Crossover Condition
for j=n:1:100
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;
pm=0.001-[1:1:Size]*(0.001)/Size; %Bigger fi, smaller pm
%pm=0.0; %No mutation
%pm=0.1; %Big mutation
for i=1:1:Size
for j=1:1:3*CodeL
temp=rand;
if pm(i)>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
Kfuzzyi
Best_J=BestJ(G)
figure(1);
plot(time,BestJ);
xlabel('Times');ylabel('Best J');
figure(2);
plot(timef,rin,'r',timef,yout,'b');
xlabel('Time(s)');ylabel('rin,yout');
⛄ 运行结果
⛄ 参考文献
[1]张乃尧. 用遗传算法优化模糊控制器的隶属度参数[J]. 电气自动化, 1996, 18(1):3.
