Stabilization for a class of delayed switched inertial neural networks via non-reduced order method

Author:

Chen Xuan12ORCID,Lin Dongyun1

Affiliation:

1. Department of Automation, Xiamen University, China

2. School of Physics and Electronic Science, Guizhou Normal University, China

Abstract

This paper tackles the issue of global stabilization for a class of delayed switched inertial neural networks (SINN). Distinct from the frequently employed reduced-order technique, this paper studies SINN directly through non-reduced order method. By constructing a novel Lyapunov functional and using Barbalat Lemma, sufficient conditions for the global asymptotic stabilization issue and global exponential stabilization issue of the considered SINN are established. Numerical simulations further confirm the feasibility of the main results. The comparative research shows that global stabilization results of this paper complement and improve some existing work.

Funder

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Instrumentation

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Synchronization analysis of fuzzy inertial neural networks with time‐varying delays via non‐reduced order method;International Journal of Adaptive Control and Signal Processing;2024-06-11

2. Stability analysis based on a control adjuster for switched neural networks by trajectory similarity;Mathematical Methods in the Applied Sciences;2023-05-24

3. Bounded real lemmas for inertial neural networks with unbounded mixed delays and state-dependent switching;Communications in Nonlinear Science and Numerical Simulation;2023-05

4. Delay compensation of linear systems with multiple distributed input delays via memoryless feedback;Transactions of the Institute of Measurement and Control;2023-04-04

5. Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays;Transactions of the Institute of Measurement and Control;2023-01-17

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