A fast learning algorithm of neural network with tunable activation function

被引：0
|
作者：
Yanjun Shen
Bingwen Wang
机构：
[1] University of Science and Technology,Department of Control Science and Engineering
来源：
Science in China Series F: Information Sciences | 2004年 / 47卷
关键词：
neural networks; RLS algorithm; TAF neural model; learning algorithm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.
引用
收藏
页码：126 / 136
页数：10
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