基于PSO算哒的神经网络PID,pso-bp神经网络,matlab

clear all; close all; xite=0.50; alfa=0.05; IN=4; H=5; Out=3; ts=0.01; % wi=0.50*rands(H,IN); % wo=0.50*rands(Out,H); %结合粒子群数值优化算法的神经网络权重初始化矩阵 wi=[ -4.7730 5.0000 4.8238 -4.7085 4.9337 4.8659 -4.9651 -4.8618 4.9470 4.9485 -4.6965 4.8327 5.0000 4.9508 4.5696 4.9154 4.6047 -4.7804 5.0000 -4.7332 ]; wo=[ 4.9438 -4.6610 4.9293 4.7416 -4.9323 5.0000 4.9414 4.0693 -4.7460 5.0000 -4.9147 4.8119 4.9292 4.9434 4.9398 ]; %[wi , wo]=SPO_InitW(); wi_init_save=wi; wo_init_save=wo; wo_1=wo; wo_2=wo; wi_1=wi; wi_2=wi; %M=[10,1,10]; %激活函数系数 M=[9.9,9.8,9.4]; x=[0,0,0]; du_1=0; u_1=0; u_2=0; u_3=0; u_4=0; u_5=0;u_6=0;u_7=0; y_1=0; y_2=0; y_3=0; error_1=0; error_2=0; Oh=zeros(H,1); I=Oh; sys=tf(400,[1,50,0]); dsys=c2d(sys,ts,'z'); [num,den]=tfdata(dsys,'v'); for k=1:1:200 time(k)=k*ts; rin(k)=1.0; yout(k)=-den(2)*y_1-den(3)*y_2+num(2)*u_1+num(3)*u_2; error(k)=rin(k)-yout(k); X(1)=error(k)-error_1; X(2)=error(k); X(3)=error(k)-2*error_1+error_2; xii=[X(1),X(2),X(3),1]; xi=xii/norm(xii); epid=[X(1);X(2);X(3)]; %%%前向传播---------------------------------------- net2=xi*(wi'); for j=1:1:H Oh(j)=( exp( net2(j)-exp(-net2(j)) ) )/(exp( net2(j)+exp(-net2(j)) )); end net3=wo*Oh; for l=1:1:Out K(l)=exp(net3(l))/(exp(net3(l))+exp(-net3(l))); %K(l)=M*net3(l); end kp(k)=M(1)*K(1); ki(k)=M(2)*K(2); kd(k)=M(3)*K(3); Kpid=[kp(k),ki(k),kd(k)]; du(k)=Kpid*epid; u(k)=u_1+du(k); if u(k)>10 u(k)=10; end if u(k)<-10 u(k)=-10; end %%%后向传播------------------------------------------------ dyu(k)=sign((yout(k)-y_1)/(du(k)-du_1+0.0001)); for j=1:1:Out dK(j)=1/(exp(net3(j))+exp(-net3(j))); %dK(j)=M; end for l=1:1:Out delta3(l)=error(k)*dyu(k)*epid(l)*dK(l); end for l=1:1:Out for i=1:1:H d_wo=xite*delta3(l)*Oh(i)+alfa*(wo_1-wo_2); end end wo=wo_1+d_wo+alfa*(wo_1-wo_2); %更新M系数 % for h = 1:Out % dM(h)=error(k)*K(h); % M(h)=M(h)+rite*dM(h); % end for i=1:1:H dO(i)=4/(exp(net2(i))+exp(-net2(i)))^2; end segma=delta3*wo; for i=1:1:H delta2(i)=dO(i)*segma(i); end d_wi=xite*delta2'*xi; wi=wi_1+d_wi+alfa*(wi_1-wi_2); wo_2=wo_1; wo_1=wo; wi_2=wi_1; wi_1=wi; du_1=du(k); u_7=u_6;u_6=u_5;u_5=u_4; u_4=u_3;u_3=u_2;u_2=u_1;u_1=u(k); y_2=y_1; y_1=yout(k); error_2=error_1; error_1=error(k); end figure(2); [t,y]=BPNN_PID(); plot(t,y,'g','Linewidth',2); hold on; plot(time,rin,'r','Linewidth',2); xlabel('t/s'); ylabel('rin,yout'); hold on ; plot(time,yout,'c','Linewidth',2); hold on; [x,y]=classic_PID(); plot(x,y,'b','Linewidth',2); %----------------- figure(3); plot(time,error,'r','Linewidth',2); xlabel('t/s'); ylabel('error'); figure(4); plot(time,u,'r','Linewidth',2); xlabel('t/s'); ylabel('u'); figure(5); subplot(311); plot(time,kp,'r','Linewidth',2); xlabel('t/s'); ylabel('kp'); subplot(312); plot(time,ki,'g','Linewidth',2); xlabel('t/s'); ylabel('ki'); subplot(313); plot(time,kd,'b','Linewidth',2); xlabel('t/s'); ylabel('kd');