Convergence of an online gradient method for feedforward neural networks with stochastic inputs

被引：24
|
作者：
Li, ZX
Wu, W [1 ]
Tian, YL
机构：
[1] Dalian Univ Technol, Dept Appl Math, Dalian 116023, Peoples R China
[2] Huazhong Univ Sci & Technol, Wuhan 430000, Peoples R China
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2004年 / 163卷 / 01期
基金：
中国国家自然科学基金;
关键词：
feedforward neural networks; online gradient method; convergence; stochastic inputs;
D O I：
10.1016/j.cam.2003.08.062
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we study the convergence of an online gradient method for feed-forward neural networks. The input training examples are permuted stochastically in each cycle of iteration. A monotonicity and a weak convergence of deterministic nature are proved. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码：165 / 176
页数：12
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