New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays

Author:

Wang Jun1,Tian Yongqiang2,Hua Lanfeng3,Shi Kaibo456ORCID,Zhong Shouming7,Wen Shiping8

Affiliation:

1. College of Electrical and Information Engineering, Southwest Minzu University, Chengdu 610041, China

2. Huawei Technologies Co., Ltd., Chengdu 611700, China

3. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

4. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China

5. Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China

6. Data Recovery Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang 641100, China

7. School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China

8. Faculty of Engineering and Information Technology, Australian AI Institute, University of Technology Sydney, Ultimo, NSW 2007, Australia

Abstract

In this work, we are concerned with the finite-time synchronization (FTS) control issue of the drive and response delayed memristor-based inertial neural networks (MINNs). Firstly, a novel finite-time stability lemma is developed, which is different from the existing finite-time stability criteria and extends the previous results. Secondly, by constructing an appropriate Lyapunov function, designing effective delay-dependent feedback controllers and combining the finite-time control theory with a new non-reduced order method (NROD), several novel theoretical criteria to ensure the FTS for the studied MINNs are provided. In addition, the obtained theoretical results are established in a more general framework than the previous works and widen the application scope. Lastly, we illustrate the practicality and validity of the theoretical results via some numerical examples.

Publisher

MDPI AG

Subject

General Medicine

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