Analysis and Application of A One-Layer Neural Network for Solving Horizontal Linear Complementarity Problems

被引：31
|
作者：
Gao, Xingbao [1 ]
Wang, Jing [2 ]
机构：
[1] Shaanxi Normal Univ, Coll Math & Informat Sci, Xian 710062, Shaanxi, Peoples R China
[2] Beifang Univ Nationalities, Inst Informat & Syst Sci, Yinchuan 750021, Ningxia, Peoples R China
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2014年 / 7卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Horizontal linear complementarity problem; neural network; stability; application; OPTIMIZATION; CONVERGENCE; STABILITY;
D O I：
10.1080/18756891.2013.858903
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we analyze the stability and convergence of a one-layer neural network proposed by Gao and Wang, which is designed to solve a class of horizontal linear complementarity problems. The globally asymptotical stability and globally exponential stability of this network are proved strictly under mild conditions, respectively. Meanwhile, this network is applied to solve a transportation problem and a class of the absolute equations.
引用
收藏
页码：724 / 732
页数：9
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