智能控制第二版仿真程序

1、精选优质文档-倾情为你奉上精选优质文档-倾情为你奉上专心-专注-专业专心-专注-专业精选优质文档-倾情为你奉上专心-专注-专业智能控制第七单元书上源程序chap7-1.m%BP identificationclear all;close all;xite=0.50;alfa=0.05;w2=rands(6,1);w2_1=w2;w2_2=w2_1;w1=rands(2,6);w1_1=w1;w1_2=w1;dw1=0*w1;x=0,0;u_1=0;y_1=0;I=0,0,0,0,0,0;Iout=0,0,0,0,0,0;FI=0,0,0,0,0,0;ts=0.001;for k=1:1:100

2、0 time(k)=k*ts;u(k)=0.50*sin(3*2*pi*k*ts);y(k)=u_13+y_1/(1+y_12);for j=1:1:6 I(j)=x*w1(:,j); Iout(j)=1/(1+exp(-I(j);end yn(k)=w2*Iout; % Output of NNI networkse(k)=y(k)-yn(k); % Error calculationw2=w2_1+(xite*e(k)*Iout+alfa*(w2_1-w2_2);for j=1:1:6 FI(j)=exp(-I(j)/(1+exp(-I(j)2;endfor i=1:1:2 for j=1

3、:1:6 dw1(i,j)=e(k)*xite*FI(j)*w2(j)*x(i); endendw1=w1_1+dw1+alfa*(w1_1-w1_2);%Jacobian%yu=0;for j=1:1:6 yu=yu+w2(j)*w1(1,j)*FI(j);enddyu(k)=yu;x(1)=u(k);x(2)=y(k);w1_2=w1_1;w1_1=w1;w2_2=w2_1;w2_1=w2;u_1=u(k);y_1=y(k);endfigure(1);plot(time,y,r,time,yn,b);xlabel(times);ylabel(y and yn);figure(2);plot

4、(time,y-yn,r);xlabel(times);ylabel(error);figure(3);plot(time,dyu);xlabel(times);ylabel(dyu);chap7-2a.m%BP Training for MIMO and Multi-samplesclear all;close all;xite=0.50;alfa=0.05;w2=rands(6,2);w2_1=w2;w2_2=w2_1;w1=rands(3,6);w1_1=w1;w1_2=w1;dw1=0*w1;I=0,0,0,0,0,0;Iout=0,0,0,0,0,0;FI=0,0,0,0,0,0;O

5、UT=2;k=0;E=1.0;NS=3;while E=1e-020k=k+1; times(k)=k;for s=1:1:NS %MIMO Samples xs=1,0,0; 0,1,0; 0,0,1; %Ideal Inputys=1,0; 0,0.5; 0,1; %Ideal Outputx=xs(s,:);for j=1:1:6 I(j)=x*w1(:,j); Iout(j)=1/(1+exp(-I(j);endyl=w2*Iout;yl=yl;el=0;y=ys(s,:);for l=1:1:OUT el=el+0.5*(y(l)-yl(l)2; %Output errorendes

6、(s)=el;E=0;if s=NS for s=1:1:NS E=E+es(s); endendey=y-yl;w2=w2_1+xite*Iout*ey+alfa*(w2_1-w2_2);for j=1:1:6 S=1/(1+exp(-I(j); FI(j)=S*(1-S);endfor i=1:1:3 for j=1:1:6 dw1(i,j)=xite*FI(j)*x(i)*(ey(1)*w2(j,1)+ey(2)*w2(j,2); endendw1=w1_1+dw1+alfa*(w1_1-w1_2);w1_2=w1_1;w1_1=w1;w2_2=w2_1;w2_1=w2;end %End

7、 of forEk(k)=E;end %End of whilefigure(1);plot(times,Ek,r);xlabel(k);ylabel(E);save wfile w1 w2;chap7-2b.m%Test BPclear all;load wfile w1 w2;%N Samplesx=0.970,0.001,0.001; 0.000,0.980,0.000; 0.002,0.000,1.040; 0.500,0.500,0.500; 1.000,0.000,0.000; 0.000,1.000,0.000; 0.000,0.000,1.000;for i=1:1:7for

8、j=1:1:6 I(i,j)=x(i,:)*w1(:,j); Iout(i,j)=1/(1+exp(-I(i,j);endendy=w2*Iout;y=ychap7-3.m%RBF identificationclear all;close all;alfa=0.05;xite=0.5; x=0,0;b=1.5*ones(4,1); c=0.5*ones(2,4); w=rands(4,1); w_1=w;w_2=w_1;c_1=c;c_2=c_1;b_1=b;b_2=b_1;d_w=0*w;d_b=0*b;y_1=0;ts=0.001;for k=1:1:2000 time(k)=k*ts;

9、u(k)=0.50*sin(1*2*pi*k*ts);y(k)=u(k)3+y_1/(1+y_12); x(1)=u(k);x(2)=y_1; for j=1:1:4 h(j)=exp(-norm(x-c(:,j)2/(2*b(j)*b(j);endym(k)=w*h;em(k)=y(k)-ym(k);for j=1:1:4 d_w(j)=xite*em(k)*h(j); d_b(j)=xite*em(k)*w(j)*h(j)*(b(j)-3)*norm(x-c(:,j)2; for i=1:1:2 d_c(i,j)=xite*em(k)*w(j)*h(j)*(x(i)-c(i,j)*(b(j

10、)-2); endend w=w_1+ d_w+alfa*(w_1-w_2); b=b_1+d_b+alfa*(b_1-b_2); c=c_1+d_c+alfa*(c_1-c_2); %Jacobian%yu=0;for j=1:1:4yu=yu+w(j)*h(j)*(c(1,j)-x(1)/b(j)2; enddyu(k)=yu;y_1=y(k); w_2=w_1;w_1=w; c_2=c_1;c_1=c; b_2=b_1;b_1=b;endfigure(1);plot(time,y,r,time,ym,b);xlabel(time(s);ylabel(y and ym); figure(2

11、);plot(time,y-ym,r);xlabel(time(s);ylabel(identification error);figure(3);plot(time,dyu,r);xlabel(times);ylabel(dyu);chap7-4.m%Diagonal Recurrent Neural Networkclear all;close all;wd=rands(7,1);wo=rands(7,1);wi=rands(3,7); xj=zeros(7,1);xj_1=xj;u_1=0;y_1=0;xitei=0.35; xited=0.35; xiteo=0.35;ts=0.001

12、;for k=1:1:1000time(k)=k*ts;u(k)=0.5*sin(4*pi*k*ts);y(k)=u_13+y_1/(1+y_12);Ini=u_1,y_1,1;for j=1:1:7 sj(j)=Ini*wi(:,j)+wd(j)*xj(j);endfor j=1:1:7 xj(j)=(1-exp(-sj(j)/(1+exp(-sj(j);endPj=0*xj;for j=1:1:7 Pj(j)=wo(j)*(1+xj(j)*(1-xj(j)*xj_1(j);endQij=0*wi;for j=1:1:7 for i=1:1:3 Qij(i,j)=wo(j)*(1+xj(j)*(1-xj(j)*Ini(i); endend ymk=0;for j=1:1:7 ymk=ymk+xj(j)*wo(j);endym(k)=ymk;e(k)=y(k)-ym(k);wo=wo+xiteo*e(k)*xj;wd=wd+xited*e(