A delayed neural network for solving linear projection equations and its analysis

被引：62
作者：
Liu, QS [1 ]
Cao, JD
Xia, YS
机构：
[1] SE Univ, Dept Math, Nanjing 210096, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Dept Math Appl, Nanjing 210003, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 04期
基金：
中国国家自然科学基金;
关键词：
asymptotical stability; delayed neural networks; exponential stability; linear matrix inequality (LMI); Lyapunov-Krasovskii functional; quadratic programming;
D O I：
10.1109/TNN.2005.849834
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 present a delayed neural network approach to solve linear projection equations. The Lyapunov-Krasovskii theory for functional differential equations and the linear matrix inequality (LMI) approach are employed to analyze the global asymptotic stability and global exponential stability of the delayed neural network. Compared with the existing linear projection neural network, theoretical results and illustrative examples show that the delayed neural network can effectively solve a class of linear projection equations and some quadratic programming problems.
引用
收藏
页码：834 / 843
页数：10
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