function ddd
p=[ 40 40 40 60 60 60 80 80 80;
    30 60 90 30 60 90 30 60 90;
    20 30 40 30 40 20 40 20 30;
    5  7  9  9  5  7  7  9  5];
t=[1.72, 3.15, 3.10, 3.32, 3.69, 3.99, 4.62, 5.083, 4.79];
net=newff(minmax(p),[20,1],{'tansig','purelin'},'traingdm');
%  设置训练参数
net.trainParam.show = 50;
net.trainParam.lr = 0.05;
net.trainParam.mc = 0.9;
net.trainParam.epochs = 1000;
net.trainParam.goal = 1e-3;
net.trainfcn='trainbfg';
[net,tr]=train(net,p,t);
% p_test=[35 55 70 80;
%         40 40 70 70;
%         30 20 40 30;
%         6  6  8  8];
% A=sim(net,p_test);
lb=[1,1,1,1];
ub=[100,100,50,20];
options = gaoptimset;
x0=ga(@(x)fun1(x,net),4,[],[],[],[],lb,ub,@nonlcon,1:4,options)
tm=sim(net,x0')
end


function t=fun1(x,net)
x=x';
t=-sim(net,x);
end

function [c,ceq]=nonlcon(x)
c=[];
ceq=[];
end

2楼: Originally posted by nono2009 at 2012-09-10 16:41:56
直接使用MATLAB的神经网络工具箱和遗传算法工具箱

3楼: Originally posted by csgt0 at 2012-09-10 16:45:26
上次帮人写的，你看看吧。

function ddd
p=;
t=;
net=newff(minmax(p),,{'tansig','purelin'},'traingdm');
%  设置训练参数
net.trainParam.show = 50;
net.trainParam.lr = 0.05;
net.trainParam.mc =  ...