Exponential stability analysis for delayed complex-valued memristor-based recurrent neural networks

被引：0
|
作者：
Ziye Zhang
Xiaoping Liu
Chong Lin
Shaowei Zhou
机构：
[1] Shandong University of Science and Technology,College of Mathematics and Systems Science
[2] Lakehead University,Department of Electrical Engineering
[3] Shandong Jianzhu University,School of Information and Electrical Engineering
[4] Qingdao University,Institute of Complexity Science
来源：
Neural Computing and Applications | 2019年 / 31卷
关键词：
Complex-valued memristor-based recurrent neural networks (CVMRNNs); Global exponential stability; Time delays; -matrix;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The exponential stability problem for complex-valued memristor-based recurrent neural networks (CVMRNNs) with time delays is studied in this paper. As an extension of real-valued memristor-based recurrent neural networks, CVMRNNs can be separated into real and imaginary parts and an equivalent real-valued system is formed. By constructing a novel Lyapunov function, a new sufficient condition to guarantee the existence, uniqueness, and global exponential stability of the equilibrium point for complex-valued systems is given in terms of M-matrix. The effectiveness of the theoretical result is shown by two numerical examples.
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页码：1893 / 1903
页数：10
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