Global exponential synchronization of complex-valued recurrent neural networks in presence of uncertainty along with time-varying bounded and unbounded delay terms

被引：10
作者：
Kumar, Ankit [1 ]
Das, Subir [1 ]
Rajeev [1 ]
Yadav, Vijay K. [2 ]
机构：
[1] Indian Inst Technol BHU, Dept Math Sci, Varanasi 221005, India
[2] Nirma Univ, Inst Technol, Dept Math & Human, Ahmadabad 382481, India
来源：
INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL | 2022年 / 10卷 / 03期
关键词：
Complex valued recurrent neural networks; Time-varying delay terms; Matrix measure method; Lyapunov stability theory; Halanay inequality; FINITE-TIME; ASYMPTOTIC STABILITY; OPTIMIZATION SUBJECT; STABILIZATION; CRITERIA;
D O I：
10.1007/s40435-021-00838-9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this article, the global exponential synchronization criteria of the complex-valued recurrent neural networks (CVRNNs) in the presence of uncertain parameters with time-varying bounded and unbounded delay terms have been investigated. Based on Halanay inequality and matrix measure approach, the global exponential synchronization is studied for two cases. The first case is the synchronization of CVRNNs in the presence of uncertain parameters with time-varying bounded and unbounded delay terms and second one is the concerned synchronization in the absence of uncertain terms with same bounded and unbounded time-varying delay terms. The synchronization of the addressed complex-valued neural networks is achieved with the help of Lyapunov functional, and several sufficient criteria and theorems. Finally, two numerical examples are taken to show the viability and unwavering quality of our theoretical results for various cases.
引用
收藏
页码：902 / 916
页数：15
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