rbfnet.m

根据神经网络的相关原理

P=[-1:0.1:1];
T=[-0.9602 -0.577 -0.0729 0.3771 0.6405 0.66 0.4609 0.1336 -0.2013 -0.4344 -0.5 -0.393 -0.1647 0.0988 0.3072 0.396 0.3449 0.1816 -0.0312 -0.2189 -0.3201];
for i=1:5
    net=newrbe(P,T,i);   %创建5个RBF网络，分布密度分别为1 2 3 4 5
    y(i,:)=sim(net,P);
end
plot(1:21,y(1,:)-T); %绘制误差曲线
hold on
plot(1:21,y(2,:)-T,'+');
hold on
plot(1:21,y(3,:)-T,'.') ;
hold on
plot(1:21,y(4,:)-T,'r--');
hold on
plot(1:21,y(5,:)-T,'g-.');
legend('spd=1','spd=2','spd=3','spd=4','spd=5')