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

被引：0
|
作者：
Li, ZX
Wu, W [1 ]
Feng, GR
Lu, HF
机构：
[1] Dalian Univ Technol, Dept Appl Math, Dalian 116023, Peoples R China
[2] Dalian Maritime Univ, Dept Math, Dalian 116000, Peoples R China
[3] Shanghai Jiao Tong Univ, Dept Math, Shanghai 200000, Peoples R China
来源：
ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS | 2005年 / 3610卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An online gradient method for BP neural networks is presented and discussed. The input training examples are permuted stochastically in each cycle of iteration. A monotonicity and a weak convergence of deterministic nature for the method are proved.
引用
收藏
页码：720 / 729
页数：10
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