Am activation function adapting training algorithm for sigmoidal feedforward networks

被引：50

作者：

Chandra, P ^{[1
]}

Singh, Y ^{[1
]}

机构：

[1] GGS Indraprastha Univ, Sch Informat Technol, Delhi 110006, India

来源：

NEUROCOMPUTING | 2004年 / 61卷

关键词：

feedforward artificial neural networks; sigmoidal activation; squashing function; self-adaptation;

D O I：

10.1016/j.neucom.2004.04.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The universal approximation results for sigmoidal feedforward artificial neural networks do not recommend a preferred activation function. In this paper a new activation function adapting algorithm is proposed for sigmoidal feedforward neural network training. The algorithm is compared against the backpropagation algorithm on four function approximation tasks. The results demonstrate that the proposed algorithm can be an order of magnitude faster than the backpropagation algorithm. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：429 / 437

页数：9

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