BP和RBF神经网络比较

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对比

算法流程图

算例分析

%BP identification%网络结构：2-6-1%权值W1、W2为[-1,1]之间的随机值，η=0.50，α=0.05clear 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=1:1:1000   time(k)=k*ts;u(k)=0.50*sin(3*2*pi*k*ts);y(k)=u_1^3+y_1/(1+y_1^2); 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 networks e(k)=y(k)-yn(k);    % Error calculation w2=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;end for i=1:1:2   for j=1:1:6      dw1(i,j)=e(k)*xite*FI(j)*w2(j)*x(i);   endendw1=w1_1+dw1+alfa*(w1_1-w1_2); 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(time,y-yn,'r');xlabel('times');ylabel('error');

输出：

%RBF identification%网络结构：2-4-1%初始值取C=[0.5 0.5]’,B=[1.5 1.5 1.5 1.5]’, η=0.50，α=0.05clear 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;u(k)=0.50*sin(1*2*pi*k*ts); y(k)=u(k)^3+y_1/(1+y_1^2);   x(1)=u(k);x(2)=y(k);   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)^-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);   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);plot(time,y-ym,'r');xlabel('time(s)');ylabel('identification error');

输出：

参考书籍：

刘金琨《智能控制》 PDF下载链接：http://pan.baidu.com/s/1dFxdoqt 密码：ehom

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