一种估计前馈神经网络中隐层神经元数目的新方法

前馈神经网络中隐层神经元的数目一般凭经验的给出，这种方法往往造成隐单元数目的不足或过甚，从而导致网络存储容量不够或出现学习过拟现象，本研究提出了一种基于信息熵的估计三层前馈神经网络隐结点数目的方法，该方法首先利用训练集来训练具有足够隐单元数目的初始神经网络，然后计算训练集中能被训练过的神经网络正确识别的样本在隐层神经元的激活值，并对其进行排序，计算这些激活值的各种划分的信息增益，从而构造能将整个样本空间正确划分的决策树，最后遍历整棵树寻找重要的相关隐层神经元，并删除冗余无关的其它隐单元，从而估计神经网络中隐层神经元的较佳数目，文章最后以构造用于茶叶品质评定的具有较佳隐单元数目的神经网络为例，介绍本方法的使用，结果表明，本方法能有效估计前馈神经网络的隐单元数目。

A Novel Method for Estimating the Number of Hidden Neurons of the Feedforward Neural Networks

The number of hidden units of the feed-forward neural networks is generally decided on the basis of experience. The method usually results in the lack or redundancy of hidden neurons, and causes the shortage of capacity for storing information or learning overmuch. This research proposes a new method for estimating the number of hidden neurons based on decision-tree algorithm. Firstly, an initial neural network with enough hidden neurons should be trained by a set of training samples. Second, the activation values of hidden neurons should be calculated by inputting the training samples that can be identified correctly by the trained neural network. Third, all kinds of partitions should be tried and its information gain should be calculated, and then a decision-tree for correctly partitioning the whole sample space can be constructed. Finally, the important and related hidden neurons that are included in the tree can be found by searching the whole tree, and other redundant hidden neurons can be deleted. Thus, the number of hidden neurons can be decided. In the case of building a neural network with the optimal number of hidden units for tea quality evaluation, the proposed method is applied. And the result demonstrates its effectiveness.