由于笔者论文的需要，总结一些神经网络算法在matlab中的专有名词。
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]。
