rbfapproach-b.m

基于遗传算法优化的RBF网络逼近程序

%RBF identification
function [p,BsJ]=rbf_gaf(p,BsJ)
ts=0.001;

alfa=0.05; %动量因子  
xite=0.85; %学习速率     
x=[0,0]';  %网络输入信号：u(k)、y(k)

b=[p(1);p(2);p(3)];   %b,c,w的初始
c=[p(4) p(5) p(6);
    p(7) p(8) p(9)];
w=[p(10);p(11);p(12)];   

w_1=w;w_2=w_1;
c_1=c;c_2=c_1;
b_1=b;b_2=b_1;
y_1=0;

for k=1:1:500
timef(k)=k*ts;
   
u(k)=sin(5*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=1:1:3
    h(j)=exp(-norm(x-c(:,j))^2/(2*b(j)*b(j)));
end
ym(k)=w_1'*h';
e(k)=y(k)-ym(k);

d_w=0*w;d_b=0*b;d_c=0*c;
for j=1:1:3
   d_w(j)=xite*e(k)*h(j);
   d_b(j)=xite*e(k)*w(j)*h(j)*(b(j)^-3)*norm(x-c(:,j))^2;
  for i=1:1:2
   d_c(i,j)=xite*e(k)*w(j)*h(j)*(x(i)-c(i,j))*(b(j)^-2);
  end
end
   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;
   end
   
B=0;
for i=1:1:500
   Ji(i)=abs(e(i));
   B=B+100*Ji(i);   
end
BsJ=B;