%% 该代码为基于pso算法优化的PID神经网络的系统控制算法
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% <table border="0" width="600px" id="table1"> <tr> <td><b><font size="2">该案例作者申明：</font></b></td> </tr> <tr><td><span class="comment"><font size="2">1：本人长期驻扎在此<a target="_blank" href="http://www.ilovematlab.cn/forum-158-1.html"><font color="#0000FF">板块</font></a>里，对该案例提问，做到有问必答。本套书籍官方网站为：<a href="http://video.ourmatlab.com">video.ourmatlab.com</a></font></span></td></tr><tr> <td><font size="2">2：点此<a href="http://union.dangdang.com/transfer/transfer.aspx?from=P-284318&backurl=http://www.dangdang.com/">从当当预定本书</a>：<a href="http://union.dangdang.com/transfer/transfer.aspx?from=P-284318&backurl=http://www.dangdang.com/">《Matlab神经网络30个案例分析》</a>。</td></tr><tr> <td><p class="comment"></font><font size="2">3</font><font size="2">：此案例有配套的教学视频，视频下载方式<a href="http://video.ourmatlab.com/vbuy.html">video.ourmatlab.com/vbuy.html</a></font><font size="2">。 </font></p></td> </tr> <tr> <td><span class="comment"><font size="2"> 4：此案例为原创案例，转载请注明出处（《Matlab神经网络30个案例分析》）。</font></span></td> </tr> <tr> <td><span class="comment"><font size="2"> 5：若此案例碰巧与您的研究有关联，我们欢迎您提意见，要求等，我们考虑后可以加在案例里。</font></span></td> </tr> </table>
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%% 清空环境变量
clc
clear
%% 粒子初始化
%粒子群算法中的两个参数
c1=1.49445;
c2=1.49445;
%最大最小权值
wmax=0.9;
wmin=0.1;
%最大最小速度
Vmax=0.03;
Vmin=-0.03;
%最大最小个体
popmax=0.3;
popmin=-0.3;
maxgen=50; % 进化次数
sizepop=20; %种群规模
%随机产生一个种群
for i=1:sizepop
pop(i,:)=0.03*rand(1,45); %个体编码
fitness(i)=fun(pop(i,:)); %染色体的适应度
V(i,:)=0.003*rands(1,45); %初始化速度
end
%% 初始种群极值
%找最好的染色体
[bestfitness bestindex]=min(fitness);
zbest=pop(bestindex,:); %全局最佳
gbest=pop; %个体最佳
fitnessgbest=fitness; %个体最佳适应度值
fitnesszbest=bestfitness; %全局最佳适应度值
%% 迭代寻优
for i=1:maxgen
i;
for j=1:sizepop
w=(wmax-wmin)*(i-1)/(maxgen)+wmin; %权值线性变化
V(j,:)=w*V(j,:) + c1*rand*(gbest(j,:) - pop(j,:)) + c2*rand*(zbest - pop(j,:)); %速度更新
V(j,find(V(j,:)>Vmax))=Vmax; %小于最大速度
V(j,find(V(j,:)<Vmin))=Vmin; %大于最小速度
%种群更新
pop(j,:)=pop(j,:)+0.5*V(j,:);
for k=1:45
if rand>0.95
pop(j,k)=0.3*rand; %自适应变异
end
end
pop(j,find(pop(j,:)>popmax))=popmax; %小于个体最大值
pop(j,find(pop(j,:)<popmin))=popmin; %大于个体最小值
%适应度值
fitness(j)=fun(pop(j,:));
end
for j=1:sizepop
%个体极值更新
if fitness(j)<fitnessgbest(j)
gbest(j,:) = pop(j,:);
fitnessgbest(j) = fitness(j);
end
%全局极值更新
if fitness(j)<fitnesszbest
zbest = pop(j,:);
fitnesszbest = fitness(j);
end
end
%记录最优适应度值
yy(i)=fitnesszbest;
end
%% 最优个体控制
figure(1)
plot(yy)
title('粒子群算法进化过程');
xlabel('进化代数');ylabel('适应度');
individual=zbest;
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
for k=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<ynmin))=ynmin;
%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<xpmin))=xpmin;
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<qimin))=qimin;
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=[0 0 0];
xd_1=[x1i_1(3),x2i_1(3),x3i_1(3)];
qd=xd-xd_1;
qd(find(qd>qdmax))=qdmax;
qd(find(qd<qdmin))=qdmin;
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<uhmin))=uhmin;
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];
for t=1:1:3
delta2(1:3,t)=error(1:3).*sign(ypc(1:3)./(uhc(t)+0.00000001));
end
for j=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);
for j=1:1:3
for p=1:1:3
alter(j)=alter(j)+delta2(p,:)*wshi2_1(:,j);
end
end
for p=1:1:3
dws1(p,:)=alter(p)*s1(p,:);
end
w11=w11+rate1*dws1;
%调整w12
for j=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);
for j=1:1:3
for p=1:1:3
alter2(j)=alter2(j)+delta2(p,:)*wshi2_2(:,j);
end
end
for p=1:1:3
dws2(p,:)=alter2(p)*s2(p,:);
end
w12=w12+rate1*dws2;
%调整w13
for j=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);
for j=1:1:3
for p=1:1:3
alter3(j)=(alter3(j)+delta2(p,:)*wshi2_3(:,j));
end
end
for p=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(2)
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
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~$ATLAB 神经网络43个案例分析》目录.docx 162B
chapter_sh.html 24KB Chapter_ClassifyRegressUsingLibsvm.html 15KB BPDLX.m 6KB MPIDDLX.m 6KB draw.m 6KB MPID.m 5KB chapter_sh.m 5KB MPIDCS.m 5KB fun.m 5KB PSO.m 5KB Genetic.m 4KB chapter1_1.m 4KB PSO.m 4KB BP.m 3KB BP_Hidden.m 3KB chapter2_1.m 3KB Genetic.m 3KB Chapter_ClassifyRegressUsingLibsvm.m 2KB PSOMutation.m 2KB Mutation.m 2KB Cross.m 2KB Cross.m 2KB Mutation.m 2KB Decode.m 1KB fun.m 1013B Select.m 853B Select.m 823B Code.m 420B Code.m 420B fun.m 326B test.m 291B fun.m 281B test.m 278B data.m 135B data.m 134B pso.m 9KB chapter_sh.mat 215KB data4.mat 91KB data1.mat 91KB data3.mat 91KB data2.mat 91KB data1.mat 90KB data.mat 90KB data.mat 45KB data.mat 45KB heart_scale.mat 28KB net.mat 535B
chapter_sh_05.png 22KB chapter_sh_04.png 18KB chapter_sh_06.png 17KB chapter_sh_03.png 17KB chapter_sh_07.png 12KB chapter_sh_08.png 12KB chapter_sh_09.png 10KB chapter_sh_02.png 10KB chapter_sh_01.png 9KB Chapter_ClassifyRegressUsingLibsvm_01.png 9KB chapter_sh.png 4KB Chapter_ClassifyRegressUsingLibsvm.png 2KB
chapter20.rar 2.59MB chapter1.rar 369KB chapter18.rar 353KB chapter17.rar 344KB chapter41.rar 220KB chapter16.rar 218KB chapter15.rar 198KB chapter4.rar 187KB chapter19.rar 123KB chapter40.rar 113KB chapter38.rar 87KB chapter42.rar 65KB chapter7.rar 65KB chapter5.rar 60KB chapter3.rar 53KB chapter2.rar 48KB chapter21.rar 44KB chapter22.rar 37KB chapter14.rar 36KB chapter34.rar 26KB chapter12.rar 18KB chapter23.rar 16KB chapter24.rar 15KB chapter33.rar 14KB chapter6.rar 13KB chapter8.rar 13KB chapter13.rar 8KB chapter25.rar 5KB chapter32.rar 4KB chapter37.rar 4KB chapter35.rar 3KB chapter43.rar 842B chapter39.rar 199B
chapter27.zip 5.08MB chapter30.zip 787KB chapter36.zip 194KB chapter29.zip 137KB chapter26.zip 88KB chapter28.zip 87KB共 104 条
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