chap8（遗传算法和粒子群算法）.zip_mat文件_粒子群_遗传算法

clear all; close all; load Fi_file; Fi=F_iden; %静态摩擦力矩估计值 w=[-1:0.05:1]'; %取一组恒定转速 N=size(w,1); Size=200; %种群规模 CodeL=8; %参数个数 MinX=zeros(CodeL,1); %参数搜索范围 MaxX(1:2)=ones(2,1); MaxX(3:4)=0.1*ones(2,1); MaxX(5:6)=ones(2,1); MaxX(7:8)=0.1*ones(2,1); MaxX=MaxX'; for i=1:1:CodeL %十进制浮点编码 kxi(:,i)=MinX(i)+(MaxX(i)-MinX(i))*rand(Size,1); end G=10000; %最大遗传代数 BsJ=0; for kg=1:1:G time(kg)=kg; if mod(kg,1000)==1 kg end %*********计算个体适应度值********** for i=1:1:Size J=0; kx=kxi(i,:); %静态摩擦参数的辨识值 Fcp=kx(1); Fsp=kx(2); alfap=kx(3); Vsp=kx(4); Fcm=kx(5); Fsm=kx(6); alfam=kx(7); Vsm=kx(8); for j=1:1:N %摩擦力矩的辨识值 if w(j)>0 F_GA(j)=(Fcp+(Fsp-Fcp)*exp(-(w(j)/Vsp)^2))*sign(w(j))+alfap*w(j); else F_GA(j)=(Fcm+(Fsm-Fcm)*exp(-(w(j)/Vsm)^2))*sign(w(j))+alfam*w(j); end Ji(j)=Fi(j)-F_GA(j); J=J+0.5*Ji(j)*Ji(j); end BsJi(i)=J; %目标函数值 end [OderJi,IndexJi]=sort(BsJi); BestJ(kg)=OderJi(1); %目标函数极小值 Ji=BsJi; Cm=max(Ji); %个体适应度值 fi=1./Ji; [Oderfi,Indexfi]=sort(fi); Bestfi(kg)=Oderfi(Size); %最大个体适应度值 BestS=kxi(Indexfi(Size),:); %***********选择操作*************** fi_sum=sum(fi); fi_Size=(Oderfi/fi_sum)*Size; fi_S=floor(fi_Size); 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; end k=1; for i=Size:-1:1 for j=1:1:fi_S(i) TempE(k,:)=kxi(Indexfi(i),:); k=k+1; end end kxi=TempE; %*************交叉操作************ Pc=0.90; %交叉概率 for i=1:2:(Size-1) temp=rand; if Pc>temp alfa=rand; %交叉方法 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; %保存最优个体 kxi=TempE; %**************变异操作************* 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) %变异方法 TempE(i,j)=Mean(j)+Dif(j)*(rand-0.5); end end end TempE(Size,:)=BestS; %保存最优个体 kxi=TempE; %********************************** end display('true value: 0.15 0.60 0.02 0.05 0.20 0.70 0.03 0.05'); BestS %最佳个体 Best_J=BestJ(G) %最大目标函数值 figure(1); %辨识前后的Stribeck曲线 plot(w,F,'xr'); hold on plot(w,F_GA,'-b'); xlabel('Speed');ylabel('Friction moment'); legend('Practical value by Test','Identified value by GA'); hold off figure(2); %最大目标函数值变化曲线 plot(time,BestJ,'r'); xlabel('Times');ylabel('Best J');