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Exponential stability of memristor-based synchronous switching neural networks with time delays
被引:2
|作者:
Jiang, Yinlu
[1
]
Li, Chuandong
[1
]
机构:
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China
关键词:
Memristor-based synchronous switching neural networks;
exponential stability;
time delays;
Lyapunov functions;
VARYING DELAYS;
LMI APPROACH;
PROGRAMMING-PROBLEMS;
DEPENDENT STABILITY;
GLOBAL STABILITY;
DISCRETE;
SYSTEMS;
SYNCHRONIZATION;
D O I:
10.1142/S1793524516500169
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
In this paper, we study the existence, uniqueness and stability of memristor-based synchronous switching neural networks with time delays. Several criteria of exponential stability are given by introducing multiple Lyapunov functions. In comparison with the existing publications on simplice memristive neural networks or switching neural networks, we consider a system with a series of switchings, these switchings are assumed to be synchronous with memristive switching mechanism. Moreover, the proposed stability conditions are straightforward and convenient and can reflect the impact of time delay on the stability. Two examples are also presented to illustrate the effectiveness of the theoretical results.
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页数:18
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