Global robust stability of complex-valued recurrent neural networks with time-delays and uncertainties

被引：25

作者：

Zhang, Wei ^{[1
]}

Li, Chuandong ^{[1
]}

Huang, Tingwen ^{[2
]}

机构：

[1] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China

[2] Texas A&M Univ Qatar, Dept Math, Doha, Qatar

来源：

INTERNATIONAL JOURNAL OF BIOMATHEMATICS | 2014年 / 7卷 / 02期

关键词：

Complex-valued recurrent neural networks; robust stability; global asymptotical stability; EXPONENTIAL STABILITY; CRITERION; MEMORY;

D O I：

10.1142/S1793524514500168

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This paper focuses on the existence, uniqueness and global robust stability of equilibrium point for complex-valued recurrent neural networks with multiple time-delays and under parameter uncertainties with respect to two activation functions. Two sufficient conditions for robust stability of the considered neural networks are presented and established in two new time-independent relationships between the network parameters of the neural system. Finally, three illustrative examples are given to demonstrate the theoretical results.

引用

页数：24

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