Matlab中常见的神经网络训练函数和学习函数

一、训练函数

1、traingd

Name:Gradient descent backpropagation (梯度下降反向传播算法 )

Description:triangd is a network training function that updates weight and bias values  according to gradient descent.

2、traingda

Name:Gradient descent  with adaptive learning rate backpropagation(自适应学习率的t梯度下降反向传播算法)  

Description:triangd is a network training function that updates weight and bias values  according to gradient descent with adaptive learning rate. it will return a trained net (net) and  the trianing record (tr).

3、traingdx (newelm函数默认的训练函数)

name:Gradient descent with momentum and adaptive learning rate backpropagation(带动量的梯度下降的自适应学习率的反向传播算法)

Description:triangdx is a network training function that updates weight and bias values  according to gradient descent momentum and an adaptive learning rate.it will return a trained net (net) and  the trianing record (tr).

4、trainlm

Name:Levenberg-Marquardt backpropagation (L-M反向传播算法)

Description:triangd is a network training function that updates weight and bias values  according toLevenberg-Marquardt optimization. it will return a trained  net (net) and  the trianing record (tr).

注:更多的训练算法请用matlab的help命令查看。

二、学习函数

1、learngd

Name:Gradient descent weight and bias learning function (梯度下降的权值和阈值学习函数)

Description:learngd is the gradient descent weight and bias learning function, it will return the weight change dW and a new learning state.

2、learngdm 

Name:Gradient descent with momentum weight and bias learning function (带动量的梯度下降的权值和阈值学习函数)

Description:learngd is the gradient descent  with momentum weight and bias learning function, it will return the weight change dW and a new learning state.

注:更多的学习函数用matlab的help命令查看。

三、训练函数与学习函数的区别

  学习函数的输出是权值和阈值的增量,训练函数的输出是训练好的网络和训练记录,在训练过程中训练函数不断调用学习函数修正权值和阈值,通过检测设定的训练步数或性能函数计算出的误差小于设定误差,来结束训练。    
或者这么说:训练函数是全局调整权值和阈值,考虑的是整体误差的最小。学习函数是局部调整权值和阈值,考虑的是单个神经元误差的最小[1]。 参考链接:【1】 https://zhidao.baidu.com/question/1883990061249711708.html?fr=iks&word=matlab%D6%D0traingdx%BA%CDlearngdm%B5%C4%C7%F8%B1%F0&ie=gbk
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