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Article

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

by
Jun Wang
1,
Yongqiang Tian
2,
Lanfeng Hua
3,
Kaibo Shi
4,5,6,*,
Shouming Zhong
7 and
Shiping Wen
8
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
*
Author to whom correspondence should be addressed.
Mathematics 2023, 11(3), 684; https://doi.org/10.3390/math11030684
Submission received: 1 January 2023 / Revised: 23 January 2023 / Accepted: 25 January 2023 / Published: 29 January 2023
(This article belongs to the Special Issue Mathematic Control and Artificial Intelligence)
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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.
Keywords: novel finite-time stability theorems; generalized MINNs; mixed time-varying delays; new non-reduced order method novel finite-time stability theorems; generalized MINNs; mixed time-varying delays; new non-reduced order method

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MDPI and ACS Style

Wang, J.; Tian, Y.; Hua, L.; Shi, K.; Zhong, S.; Wen, S. New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays. Mathematics 2023, 11, 684. https://doi.org/10.3390/math11030684

AMA Style

Wang J, Tian Y, Hua L, Shi K, Zhong S, Wen S. New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays. Mathematics. 2023; 11(3):684. https://doi.org/10.3390/math11030684

Chicago/Turabian Style

Wang, Jun, Yongqiang Tian, Lanfeng Hua, Kaibo Shi, Shouming Zhong, and Shiping Wen. 2023. "New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays" Mathematics 11, no. 3: 684. https://doi.org/10.3390/math11030684

APA Style

Wang, J., Tian, Y., Hua, L., Shi, K., Zhong, S., & Wen, S. (2023). New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays. Mathematics, 11(3), 684. https://doi.org/10.3390/math11030684

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