An improved training algorithm for feedforward neural network learning based on terminal attractors

被引：0
|
作者：
Xinghuo Yu
Bin Wang
Batsukh Batbayar
Liuping Wang
Zhihong Man
机构：
[1] RMIT University,Platform Technologies Research Institute
[2] Southeast University,School of Automation
[3] RMIT University,School of Electrical and Computer Engineering
[4] National University of Mongolia,School of Information Technology
[5] Swinburne University of Technology,Faculty of Engineering and Industrial Sciences
来源：
Journal of Global Optimization | 2011年 / 51卷
关键词：
Feedforward neural network; Terminal attractor; Back-propagation; Training; Optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, an improved training algorithm based on the terminal attractor concept for feedforward neural network learning is proposed. A condition to avoid the singularity problem is proposed. The effectiveness of the proposed algorithm is evaluated by various simulation results for a function approximation problem and a stock market index prediction problem. It is shown that the terminal attractor based training algorithm performs consistently in comparison with other existing training algorithms.
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收藏
页码：271 / 284
页数：13
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