matlab30个案例分析案例6代码.docx
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matlab30个案例分析案例6代码.docx
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matlab30个案例分析案例6代码
Draw
%functionJ=draw(individual)
loadbestzbest
individual=zbest;
%函数功能:
画出最优粒子对应的各种图形
%individual输入粒子
%fitness输出适应度值
w11=reshape(individual(1:
6),3,2);
w12=reshape(individual(7:
12),3,2);
w13=reshape(individual(13:
18),3,2);
w21=individual(19:
27);
w22=individual(28:
36);
w23=individual(37:
45);
rate1=0.006;rate2=0.001;%学习率
k=0.3;K=3;
y_1=zeros(3,1);y_2=y_1;y_3=y_2;%输出值
u_1=zeros(3,1);u_2=u_1;u_3=u_2;%控制率
h1i=zeros(3,1);h1i_1=h1i;%第一个控制量
h2i=zeros(3,1);h2i_1=h2i;%第二个控制量
h3i=zeros(3,1);h3i_1=h3i;%第三个空置量
x1i=zeros(3,1);x2i=x1i;x3i=x2i;x1i_1=x1i;x2i_1=x2i;x3i_1=x3i;%隐含层输出
%权值初始化
k0=0.03;
%值限定
ynmax=1;ynmin=-1;%系统输出值限定
xpmax=1;xpmin=-1;%P节点输出限定
qimax=1;qimin=-1;%I节点输出限定
qdmax=1;qdmin=-1;%D节点输出限定
uhmax=1;uhmin=-1;%输出结果限定
fork=1:
1:
200
%--------------------------------网络前向计算--------------------------
%系统输出
y1(k)=(0.4*y_1
(1)+u_1
(1)/(1+u_1
(1)^2)+0.2*u_1
(1)^3+0.5*u_1
(2))+0.3*y_1
(2);
y2(k)=(0.2*y_1
(2)+u_1
(2)/(1+u_1
(2)^2)+0.4*u_1
(2)^3+0.2*u_1
(1))+0.3*y_1(3);
y3(k)=(0.3*y_1(3)+u_1(3)/(1+u_1(3)^2)+0.4*u_1(3)^3+0.4*u_1
(2))+0.3*y_1
(1);
r1(k)=0.7;r2(k)=0.4;r3(k)=0.6;%控制目标
%系统输出限制
yn=[y1(k),y2(k),y3(k)];
yn(find(yn>ynmax))=ynmax;
yn(find(yn %输入层输出 x1o=[r1(k);yn (1)];x2o=[r2(k);yn (2)];x3o=[r3(k);yn(3)]; %隐含层 x1i=w11*x1o; x2i=w12*x2o; x3i=w13*x3o; %比例神经元P计算 xp=[x1i (1),x2i (1),x3i (1)]; xp(find(xp>xpmax))=xpmax; xp(find(xp qp=xp; h1i (1)=qp (1);h2i (1)=qp (2);h3i (1)=qp(3); %积分神经元I计算 xi=[x1i (2),x2i (2),x3i (2)]; qi=[0,0,0];qi_1=[h1i (2),h2i (2),h3i (2)]; qi=qi_1+xi; qi(find(qi>qimax))=qimax; qi(find(qi h1i (2)=qi (1);h2i (2)=qi (2);h3i (2)=qi(3); %微分神经元D计算 xd=[x1i(3),x2i(3),x3i(3)]; qd=[000]; xd_1=[x1i_1(3),x2i_1(3),x3i_1(3)]; qd=xd-xd_1; qd(find(qd>qdmax))=qdmax; qd(find(qd h1i(3)=qd (1);h2i(3)=qd (2);h3i(3)=qd(3); %输出层计算 wo=[w21;w22;w23]; qo=[h1i',h2i',h3i'];qo=qo'; uh=wo*qo; uh(find(uh>uhmax))=uhmax; uh(find(uh u1(k)=uh (1);u2(k)=uh (2);u3(k)=uh(3);%控制律 %--------------------------------------网络反馈修正---------------------- %计算误差 error=[r1(k)-y1(k);r2(k)-y2(k);r3(k)-y3(k)]; error1(k)=error (1);error2(k)=error (2);error3(k)=error(3); J(k)=0.5*(error (1)^2+error (2)^2+error(3)^2);%调整大小 ypc=[y1(k)-y_1 (1);y2(k)-y_1 (2);y3(k)-y_1(3)]; uhc=[u_1 (1)-u_2 (1);u_1 (2)-u_2 (2);u_1(3)-u_2(3)]; %隐含层和输出层权值调整 %调整w21 Sig1=sign(ypc./(uhc (1)+0.00001)); dw21=sum(error.*Sig1)*qo'; w21=w21+rate2*dw21; %调整w22 Sig2=sign(ypc./(uh (2)+0.00001)); dw22=sum(error.*Sig2)*qo'; w22=w22+rate2*dw22; %调整w23 Sig3=sign(ypc./(uh(3)+0.00001)); dw23=sum(error.*Sig3)*qo'; w23=w23+rate2*dw23; %输入层和隐含层权值调整 delta2=zeros(3,3); wshi=[w21;w22;w23]; fort=1: 1: 3 delta2(1: 3,t)=error(1: 3).*sign(ypc(1: 3)./(uhc(t)+0.00000001)); end forj=1: 1: 3 sgn(j)=sign((h1i(j)-h1i_1(j))/(x1i(j)-x1i_1(j)+0.00001)); end s1=sgn'*[r1(k),y1(k)]; wshi2_1=wshi(1: 3,1: 3); alter=zeros(3,1); dws1=zeros(3,2); forj=1: 1: 3 forp=1: 1: 3 alter(j)=alter(j)+delta2(p,: )*wshi2_1(: j); end end forp=1: 1: 3 dws1(p,: )=alter(p)*s1(p,: ); end w11=w11+rate1*dws1; %调整w12 forj=1: 1: 3 sgn(j)=sign((h2i(j)-h2i_1(j))/(x2i(j)-x2i_1(j)+0.0000001)); end s2=sgn'*[r2(k),y2(k)]; wshi2_2=wshi(: 4: 6); alter2=zeros(3,1); dws2=zeros(3,2); forj=1: 1: 3 forp=1: 1: 3 alter2(j)=alter2(j)+delta2(p,: )*wshi2_2(: j); end end forp=1: 1: 3 dws2(p,: )=alter2(p)*s2(p,: ); end w12=w12+rate1*dws2; %调整w13 forj=1: 1: 3 sgn(j)=sign((h3i(j)-h3i_1(j))/(x3i(j)-x3i_1(j)+0.0000001)); end s3=sgn'*[r3(k),y3(k)]; wshi2_3=wshi(: 7: 9); alter3=zeros(3,1); dws3=zeros(3,2); forj=1: 1: 3 forp=1: 1: 3 alter3(j)=(alter3(j)+delta2(p,: )*wshi2_3(: j)); end end forp=1: 1: 3 dws3(p,: )=alter2(p)*s3(p,: ); end w13=w13+rate1*dws3; %参数更新 u_3=u_2;u_2=u_1;u_1=uh; y_2=y_1;y_1=yn; h1i_1=h1i;h2i_1=h2i;h3i_1=h3i; x1i_1=x1i;x2i_1=x2i;x3i_1=x3i; end time=0.001*(1: k); figure (1) subplot(3,1,1) plot(time,r1,'r-',time,y1,'b-'); title('PID神经元网络控制'); ylabel('被控量1'); legend('控制目标','实际输出','fontsize',12); subplot(3,1,2) plot(time,r2,'r-',time,y2,'b-'); ylabel('被控量2'); legend('控制目标','实际输出','fontsize',12); axis([0,0.2,0,1]) subplot(3,1,3) plot(time,r3,'r-',time,y3,'b-'); xlabel('时间/s'); ylabel('被控量3'); legend('控制目标','实际输出','fontsize',12); print-dtiff-r600改4 figure(3) plot(time,u1,'r-',time,u2,'g-',time,u3,'b'); title('PID神经网络提供给对象的控制输入'); xlabel('时间'),ylabel('控制律'); legend('u1','u2','u3');grid figure(4) plot(time,J,'r-'); axis([0,0.1,0,0.5]);grid title('网络学习目标函数J动态曲线'); xlabel('时间');ylabel('控制误差'); %BPy1=y1; %BPy2=y2; %BPy3=y3; %BPu1=u1; %BPu2=u2; %BPu3=u3; %BPJ=J %saveBPr1r2r3BPy1BPy2BPy3BPu1BPu2BPu3BPJ Fun functionFitness=fun(individual) %函数功能: 计算个体适应度值 %individual输入粒子 %fitness输出适应度值 w11=reshape(individual(1: 6),3,2); w12=reshape(individual(7: 12),3,2); w13=reshape(individual(13: 18),3,2); w21=individual(19: 27); w22=individual(28: 36); w23=individual(37: 45); rate1=0.006;rate2=0.001;%学习率 k=0.3;K=3; y_1=zeros(3,1);y_2=y_1;y_3=y_2;%输出值 u_1=zeros(3,1);u_2=u_1;u_3=u_2;%控制率 h1i=zeros(3,1);h1i_1=h1i;%第一个控制量 h2i=zeros(3,1);h2i_1=h2i;%第二个控制量 h3i=zeros(3,1);h3i_1=h3i;%第三个空置量 x1i=zeros(3,1);x2i=x1i;x3i=x2i;x1i_1=x1i;x2i_1=x2i;x3i_1=x3i;%隐含层输出 %权值初始化 k0=0.03; %值限定 ynmax=1;ynmin=-1;%系统输出值限定 xpmax=1;xpmin=-1;%P节点输出限定 qimax=1;qimin=-1;%I节点输出限定 qdmax=1;qdmin=-1;%D节点输出限定 uhmax=1;uhmin=-1;%输出结果限定 ERROR=[]; fork=1: 1: 200 %--------------------------------网络前向计算-------------------------- %系统输出 y1(k)=(0.4*y_1 (1)+u_1 (1)/(1+u_1 (1)^2)+0.2*u_1 (1)^3+0.5*u_1 (2))+0.3*y_1 (2); y2(k)=(0.2*y_1 (2)+u_1 (2)/(1+u_1 (2)^2)+0.4*u_1 (2)^3+0.2*u_1 (1))+0.3*y_1(3); y3(k)=(0.3*y_1(3)+u_1(3)/(1+u_1(3)^2)+0.4*u_1(3)^3+0.4*u_1 (2))+0.3*y_1 (1); r1(k)=0.7;r2(k)=0.4;r3(k)=0.6;%控制目标 %系统输出限制 yn=[y1(k),y2(k),y3(k)]; yn(find(yn>ynmax))=ynmax; yn(find(yn %输入层输出 x1o=[r1(k);yn (1)];x2o=[r2(k);yn (2)];x3o=[r3(k);yn(3)]; %隐含层 x1i=w11*x1o; x2i=w12*x2o; x3i=w13*x3o; %比例神经元P计算 xp=[x1i (1),x2i (1),x3i (1)]; xp(find(xp>xpmax))=xpmax; xp(find(xp qp=xp; h1i (1)=qp (1);h2i (1)=qp (2);h3i (1)=qp(3); %积分神经元I计算 xi=[x1i (2),x2i (2),x3i (2)]; qi=[0,0,0];qi_1=[h1i (2),h2i (2),h3i (2)]; qi=qi_1+xi; qi(find(qi>qimax))=qimax; qi(find(qi h1i (2)=qi (1);h2i (2)=qi (2);h3i (2)=qi(3); %微分神经元D计算 xd=[x1i(3),x2i(3),x3i(3)]; qd=[000]; xd_1=[x1i_1(3),x2i_1(3),x3i_1(3)]; qd=xd-xd_1; qd(find(qd>qdmax))=qdmax; qd(find(qd h1i(3)=qd (1);h2i(3)=qd (2);h3i(3)=qd(3); %输出层计算 wo=[w21;w22;w23]; qo=[h1i',h2i',h3i'];qo=qo'; uh=wo*qo; uh(find(uh>uhmax))=uhmax; uh(find(uh u1(k)=uh (1);u2(k)=uh (2);u3(k)=uh(3);%控制律 %--------------------------------------网络反馈修正---------------------- %计算误差 error=[r1(k)-y1(k);r2(k)-y2(k);r3(k)-y3(k)]; ERROR=[ERROR,error]; error1(k)=error (1);error2(k)=error (2);error3(k)=error(3); J(k)=0.5*(error (1)^2+error (2)^2+error(3)^2);%调整大小 ypc=[y1(k)-y_1 (1);y2(k)-y_1 (2);y3(k)-y_1(3)]; uhc=[u_1 (1)-u_2 (1);u_1 (2)-u_2 (2);u_1(3)-u_2(3)]; %隐含层和输出层权值调整 %调整w21 Sig1=sign(ypc./(uhc (1)+0.00001)); dw21=sum(error.*Sig1)*qo'; w21=w21+rate2*dw21; %调整w22 Sig2=sign(ypc./(uh (2)+0.00001)); dw22=sum(error.*Sig2)*qo'; w22=w22+rate2*dw22; %调整w23 Sig3=sign(ypc./(uh(3)+0.00001)); dw23=sum(error.*Sig3)*qo'; w23=w23+rate2*dw23; %输入层和隐含层权值调整 delta2=zeros(3,3); wshi=[w21;w22;w23]; fort=1: 1: 3 delta2(1: 3,t)=error(1: 3).*sign(ypc(1: 3)./(uhc(t)+0.00000001)); end forj=1: 1: 3 sgn(j)=sign((h1i(j)-h1i_1(j))/(x1i(j)-x1i_1(j)+0.00001)); end s1=sgn'*[r1(k),y1(k)]; wshi2_1=wshi(1: 3,1: 3); alter=zeros(3,1); dws1=zeros(3,2); forj=1: 1: 3 forp=1: 1: 3 alter(j)=alter(j)+delta2(p,: )*wshi2_1(: j); end end forp=1: 1: 3 dws1(p,: )=alter(p)*s1(p,: ); end w11=w11+rate1*dws1; %调整w12 forj=1: 1: 3 sgn(j)=sign((h2i(j)-h2i_1(j))/(x2i(j)-x2i_1(j)+0.0000001)); end s2=sgn'*[r2(k),y2(k)]; wshi2_2=wshi(: 4: 6); alter2=zeros(3,1); dws2=zeros(3,2); forj=1: 1: 3 forp=1: 1: 3 alter2(j)=alter2(j)+delta2(p,: )*wshi2_2(: j); end end forp=1: 1: 3 dws2(p,: )=alter2(p)*s2(p,: ); end w12=w12+rate1*dws2; %调整w13 forj=1: 1: 3 sgn(j)=sign((h3i(j)-h3i_1(j))/(x3i(j)-x3i_1(j)+0.0000001)); end s3=sgn'*[r3(k),y3(k)]; wshi2_3=wshi(: 7: 9); alter3=zeros(3,1); dws3=zeros(3,2); forj=1: 1: 3 forp=1: 1: 3 alter3(j)=(alter3(j)+delta2(p,: )*wshi2_3(: j)); end end forp=1: 1: 3 dws3(p,: )=alter2(p)*s3(p,: ); end w13=w13+rate1*dws3; %参数更新 u_3=u_2;u_2=u_1;u_1=uh; y_2=y_1;y_1=yn; h1i_1=h1i;h2i_1=h2i;h3i_1=h3i; x1i_1=x1i;x2i_1=x2i;x3i_1=x3i; end BPoutput=[y1;y2;y3]; %计算适应度值 Fitness=0; fori=1: 100 Fitness=exp(0.01*i)*sum(abs(ERROR(: i)))+Fitness; ifi>1 forj=1: 3 erry=BPoutput(j,i)-BPoutput(j,i-1); iferry<0 Fitness=Fitness+3*abs(erry); end end end end mpid %%清空环境变量 clc clear %%网络结构初始化 rate1=0.006;rate2=0.001;%学习率 k=0.3;K=3; y_1=zeros(3,1);y_2=y_1;y_3=y_2;%输出值 u_1=zeros(3,1);u_2=u_1;u_3=u_2;%控制率 h1i=zeros(3,1);h1i_1=h1i;%第一个控制量 h2i=zeros(3,1);h2i_1=h2i;%第二个控制量 h3i=zeros(3,1);h3i_1=h3i;%第三个空置量 x1i=zeros(3,1);x2i=x1i;x3i=x2i;x1i_1=x1i;x2i_1=x2i;x3i_1=x3i;%隐含层输出 %权值初始化 k0=0.03; %第一层权值 w11=k0*rand(3,2); w12=k0*rand(3,2); w13=k0*rand(3,2); %第二层权值 w21=k0*rand(1,9); w22=k0*rand(1,9); w23=k0*rand(1,9); %值限定 ynmax=1;ynmin=-1;%系统输出值限定 xpmax=1;xpmin=-1;%P节点输出限定 qimax=1;qimin=-1;%I节点输出限定 qdmax=1;qdmin=-1;%D节点输出限定 uhmax=1;uhmin=-1;%输出结果限定 %%网络迭代优化 fork=1: 1: 200 %%控制量输出计算 %--------------------------------网络前向计算-------------------------- %系统输出 y1(k)=(0.4*y_1 (1)+u_1 (1)/(1+u_1 (1)^2)+0.2*u_1 (1)^3+0.5*u_1 (2))+0.3*y_1 (2); y2(k)=(0.2*y_1 (2)+u_1 (2)/(1+u_1 (2)^2)+0.4*u_1 (2)^3+0.2*u_1 (1))+0.3*y_1(3);
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