作业帮MATLAB BP神经网络训练P=[392,584,776,2713,3236,2783,2163;718,1043,1368,4318,5054,4483,3769];T=[1.4404,1.4722,1.4753,1.497,1.7663,1.6807,1.7426];net=newff(minmax(P),[3,1],{'tansig','purelin'},'traingdm');net.trainParam.show = 50;net.trainParam.lr
来源：学生作业帮助网 编辑：作业帮 时间：2024/05/01 20:14:00
MATLAB BP神经网络训练P=[392,584,776,2713,3236,2783,2163;718,1043,1368,4318,5054,4483,3769];T=[1.4404,1.4722,1.4753,1.497,1.7663,1.6807,1.7426];net=newff(minmax(P),[3,1],{'tansig','purelin'},'traingdm');net.trainParam.show = 50;net.trainParam.lr
MATLAB BP神经网络训练
P=[392,584,776,2713,3236,2783,2163;718,1043,1368,4318,5054,4483,3769];
T=[1.4404,1.4722,1.4753,1.497,1.7663,1.6807,1.7426];
net=newff(minmax(P),[3,1],{'tansig','purelin'},'traingdm');
net.trainParam.show = 50;
net.trainParam.lr = 0.05;
net.trainParam.mc = 0.9;
net.trainParam.epochs = 1000;
net.trainParam.goal = 1e-3;
[net,tr]=train(net,P,T);
A = sim(net,P)
得到A的结果全是一样的,是程序问题,
MATLAB BP神经网络训练P=[392,584,776,2713,3236,2783,2163;718,1043,1368,4318,5054,4483,3769];T=[1.4404,1.4722,1.4753,1.497,1.7663,1.6807,1.7426];net=newff(minmax(P),[3,1],{'tansig','purelin'},'traingdm');net.trainParam.show = 50;net.trainParam.lr
你的程序训练完毕后根本就没达到目标误差,就是说训练效果不好,不能进行预测,只有训练结果好了才能预测仿真,你再改一下隐含层神经元数或者训练和传递函数试试吧~
另外输入层的值可以归一化也可以不归一化,归一化后在仿真之前要反归一化.