Stability Analysis for Memristor-Based Complex-Valued Neural Networks with Time Delays

被引：1
|
作者：
Hou, Ping [1 ]
Hu, Jun [2 ]
Gao, Jie [3 ]
Zhu, Peican [4 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Sch Management, Nanjing 210023, Jiangsu, Peoples R China
[2] Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100080, Peoples R China
[3] Southwest Petr Univ, Sch Sci, Chengdu 610500, Sichuan, Peoples R China
[4] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
来源：
ENTROPY | 2019年 / 21卷 / 02期
基金：
国家重点研发计划;
关键词：
memristor-based complex-valued neural networks; exponential stability; time delays; EXPONENTIAL STABILITY; SYNCHRONIZATION;
D O I：
10.3390/e21020120
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
In this paper, the problem of stability analysis for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is investigated extensively. This paper focuses on the exponential stability of the MCVNNs with time-varying delays. By means of the Brouwer's fixed-point theorem and M-matrix, the existence, uniqueness, and exponential stability of the equilibrium point for MCVNNs are studied, and several sufficient conditions are obtained. In particular, these results can be applied to general MCVNNs whether the activation functions could be explicitly described by dividing into two parts of the real parts and imaginary parts or not. Two numerical simulation examples are provided to illustrate the effectiveness of the theoretical results.
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页数：12
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