matlab_Rbf智能人工实现

%====================================================================== % Copyright (C) 2005 % All rights reserved. % File name: FriedmanI.m % Notes: 应用RBF网络对 Friedman #1 进行函数逼近 % 检验反向传播的逼近效果 % 最简单的简单的梯度下降算法 % Version: 1.0 % Author: bug zhang % Date: 2005.1 %====================================================================== close all %关闭所有的窗口 clear all %清除所有的变量 k=input('训练集点数=k,k=[3000] '); if isempty(k), k=3000; end; L=input('最大隐层神经元个数=L,L=[300] '); if isempty(L), L=300; end; S=input('归一化参数=S,S=[1] '); if isempty(S), S=1; end; X1=rand(1,k); X2=rand(1,k); X3=rand(1,k); X4=rand(1,k); X5=rand(1,k); X=[X1;X2;X3;X4;X5]; Y=10.*sin(pi.*X1.*X2)+20*(X3-0.5).^2+10.*X4+5.*X5; net=newrb(X,Y,0,S,L); X1=rand(1,3000); X2=rand(1,3000); X3=rand(1,3000); X4=rand(1,3000); X5=rand(1,3000); X=[X1;X2;X3;X4;X5]; Y1=10.*sin(pi.*X1.*X2)+20.*(X3-0.5).^2+10.*X4+5.*X5; Y2=sim(net,X); R=linspace(min(Y1),max(Y1),1000); T=R; figure(2) plot(R,T,'-k',Y1,Y2,'+r')