The improvements of BP neural network learning algorithm

被引：0

作者：

Wei, WJ ^{[1
]}

Li, ZJ ^{[1
]}

Wei, LS ^{[1
]}

Zhen, H ^{[1
]}

机构：

[1] NO Jiaotong Univ, Dept Comp Sci & Technol, Beijing 100044, Peoples R China

来源：

2000 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I-III | 2000年

关键词：

neural network; BP algorithm; scale conversion; properties of networks; local minimum;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The back-propagation algorithm(BP) is a well-known method of training a multilayer Feed -Forward Artificial Neural Networks(FFANNS). Although the algorithm is successful,it has some disadvantages. Because of adopting the gradient method by BP neural network, the problems including slowly learning convergent velocity and easily converging to local minimum can not be avoided. In addition, the selection of learning factor and inertial factor affects the convergence of BP neural network, which are usually determined by experiences. Therefore the effective application of BP neural network is limited. in this paper a new method in BP algorithm to avoid local minimum was proposed by means of adding gradually training data and hidden units. In addition, the paper also proposed a new model of controllable feed-forward neural network.

引用

页码：1647 / 1649

页数：3

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