代码搜索：trainlm
找到约 183 项符合「trainlm」的源代码
代码结果 183
www.eeworm.com/read/391564/8396938
m trainlm.m
P=[0.3762 0.6084 0.4778 0.9000 0.7166 0.2992;
0.8841 0.9000 0.2851 0.6000 0.9000 0.5489;
0.7889 0.6084 0.2973 0.7000 0.6767 0.5152;
0.5508 0.3325 0.9000 0.8250 0.6821 0.5726;
0.9000 0.2261 0.837
www.eeworm.com/read/361503/10049831
gif trainlm.gif
www.eeworm.com/read/361503/10049975
h trainlm.h
/*
* MATLAB Compiler: 3.0
* Date: Sun May 13 16:47:40 2007
* Arguments: "-B" "macro_default" "-O" "all" "-O" "fold_scalar_mxarrays:on"
* "-O" "fold_non_scalar_mxarrays:on" "-O" "optimize_integ
www.eeworm.com/read/361503/10050065
c trainlm.c
/*
* MATLAB Compiler: 3.0
* Date: Sun May 13 16:47:40 2007
* Arguments: "-B" "macro_default" "-O" "all" "-O" "fold_scalar_mxarrays:on"
* "-O" "fold_non_scalar_mxarrays:on" "-O" "optimize_integ
www.eeworm.com/read/395725/8155785
m trainlm.m
p=[0.0000 0.0000 0.0000 0.0000 0.9000 0.0500 0.0000 0.0000;
0.0000 0.0000 0.0000 0.0000 0.4000 0.5000 0.0000 0.0000;
0.1000 0.8000 0.0000 0.1000 0.0000 0.0000 0.0000 0.0000;
0.1000 0.1000
www.eeworm.com/read/396828/8088526
m trainlm_snn.m
function [net, result] = trainlm_snn(net, dataLV, dataVV, dataTV)
%TRAINLM_SNN Levenberg-Marquardt backpropagation.
%
% Syntax
%
% [net, tr_info] = trainlm_snn(net, dataLV)
% [net, tr_info] = tra
www.eeworm.com/read/187956/8585637
m trainnn.m
function[w1,b1,w2,b2,ep,tr]=trainNN(p,t,s1,df,me,eg,lr)
clf reset;
[w1,b1,w2,b2]=initff(p,s1,'tansig',t,'logsig');NNTWARN OFF;
tp=[df me eg lr];
[w1,b1,w2,b2,ep,tr]=trainlm(w1,b1,'tansig',w2,b2,'p
www.eeworm.com/read/429840/8786001
m examp10_10.m
net=newff([0,1; -1,5],[8,1],{'tansig','logsig'});
net=newff([0,1; -1,5],[4 6 1],{'purelin','tansig','logsig'});
net.trainParam.epochs=300; net.trainFcn='trainlm'
www.eeworm.com/read/282792/9059937
m 2-9.m
%创建一个BP网络
net = newff([-2 2],[4 1],{'tansig','purelin'},'trainlm','learngdm','msereg');
p = [-2 -1 0 1 2];
t = [0 1 1 1 0];
y = sim(net,p)
%误差向量
e = t-y
%设置性能参数
net.performParam.ratio = 20/(
www.eeworm.com/read/378557/9223858
m 2-9.m
%创建一个BP网络
net = newff([-2 2],[4 1],{'tansig','purelin'},'trainlm','learngdm','msereg');
p = [-2 -1 0 1 2];
t = [0 1 1 1 0];
y = sim(net,p)
%误差向量
e = t-y
%设置性能参数
net.performParam.ratio = 20/(