Estimating the number of Hidden Nodes of the Single-hidden-layer Feedforward Neural Networks

被引：9
|
作者：
Cai, Guang-Wei [1 ]
Fang, Zhi [1 ]
Chen, Yue-Feng [2 ]
机构：
[1] Beijing Inst Comp Technol & Applicat, Beijing, Peoples R China
[2] 63963 Unit PLA, Beijing, Peoples R China
来源：
2019 15TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2019) | 2019年
关键词：
Single-hidden-layer feedforward neural network; hidden nodes; singular value decomposition; data normalization;
D O I：
10.1109/CIS.2019.00044
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to solve the problem that there is no effective means to find the optimal number of hidden nodes of single-hidden-layer feedforward neural network, in this paper, a method will be introduced to solve it effectively by using singular value decomposition. First, the training data need to be normalized strictly by attribute-based data normalization and sample-based data normalization. Then, the normalized data is decomposed based on the singular value decomposition, and the number of hidden nodes is determined according to main eigenvalues. The experimental results of MNIST data set and APS data set show that the feedforward neural network can attain satisfactory performance in the classification task.
引用
收藏
页码：172 / 176
页数：5
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