Global Asymptotic Stability of Periodic Solutions for Discrete Time Delayed BAM Neural Networks by Combining Coincidence Degree Theory with LMI Method

被引：6

作者：

Ren, Ling ^{[1
]}

Yi, Xuejun ^{[1
]}

Zhang, Zhengqiu ^{[1
]}

机构：

[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2019年 / 50卷 / 02期

关键词：

Periodic solutions; Delayed discrete-time BAM neural networks; Global asymptotic stability; Mawhin's continuation theorem; Linear matrix inequality; EXPONENTIAL STABILITY; NEUTRAL-TYPE; EXISTENCE;

D O I：

10.1007/s11063-018-9909-2

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 are concerned with the existence and global asymptotic stability of periodic solutions for a class of delayed discrete-time BAM neural networks. Instead of using the method of the priori estimate of periodic solutions in existing papers to study periodic solutions of neural networks, by combining Mawhin's continuation theorem of coincidence degree theory with linear matrix inequality (LMI) method as well as inequality techniques, some novel LMI-based sufficient conditions to guarantee the existence and global asymptotic stability of periodic solutions for the neural networks are established. Our results which are both dependent on time delay and external inputs of the neural networks are new and complementary to the existing papers.

引用

页码：1321 / 1340

页数：20

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