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基于RBF神经网络的线性规划仿真

已有 10315 次阅读| 2018-10-20 07:01 |系统分类:芯片设计

%clc
clear
close all

n1 = 1:2:200;
x1 = sin(n1*0.1);

n2 = 2:2:200;
x2 = sin(n2*0.1);

xn_train = n1;
dn_train = x1;
xn_test = n2;
dn_test = x2;

%---------------------------------------------------

switch 1
case 1


P = xn_train;
T = dn_train;
spread = 40;
net = newrbe(P,T,spread);

case 2

P = xn_train;
T = dn_train;
goal = 1e-12;
spread = 40;
MN = size(xn_train,2);
DF = 1;
net = newrb(P,T,goal,spread,MN,DF);

case 3

P = xn_train;
T = dn_train;
spread = 0.5;
net = newgrnn(P,T,spread);

end

err1 = sum((dn_train-sim(net,xn_train)).^2)

X = sim(net,xn_test);
err2 = sum((dn_test-X).^2)

%---------------------------------------------------

plot(1:length(n2),x2,'r+:',1:length(n2),X,'bo:')
title('+为真实值，o为预测值')