推荐回答(3个)
线性神经网络的构建:
net=newlin(PR,S,ID,LR)
PR--Rx2阶矩阵,R个输入元素的最小最大矩阵
S---输出层神经元个数
ID--输入延迟向量,默认值为[0]
IR--学习率,默认值为0.01
net = newlin([-1 1;-1 1],1); 表示设计的是一个双输入单输出线性神经网络
P = [1 2 2 3; 2 1 3 1];表示输入样本有四个,每一列就是一个输入样本
又比如假设我们期望的输出为 T=[1 2 3 4],则一个简单的神经网络如下:
>>net = newlin([-1 1;-1 1],1);%创建初始网络
P=[1 2 2 3; 2 1 3 1]%输入
T=[1 2 3 4]%期望的输出
net=newlind(P,T);%用输入和期望训练网络
Y=sim(net,P)%仿真,可以看到仿真结果Y和期望输出T的接近程度
P =
1 2 2 3
2 1 3 1
T =
1 2 3 4
Y =
0.8889 2.1667 3.0556 3.8889
楼主可以从《matlab神经网络与应用(第二版)》董长虹 开始入门神经网络的matlab实现
参考资料:《matlab神经网络与应用(第二版)》
My guess is that you created your own function mse.m that is shadowing the
mse.m included in Neural Network Toolbox and that function does not accept
and handle all the inputs that the Neural Network Toolbox's version does.
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