Stochastic Synchronization of Impulsive Reaction–Diffusion BAM Neural Networks at a Fixed and Predetermined Time-Reference-Cited by-同舟云学术

Stochastic Synchronization of Impulsive Reaction–Diffusion BAM Neural Networks at a Fixed and Predetermined Time

Published:2024-04-17 Issue:8 Volume:12 Page:1204
ISSN:2227-7390
Container-title:Mathematics
language:en
Short-container-title:Mathematics

Author:

Mahemuti Rouzimaimaiti¹²,Kasim Ehmet³^ORCID,Sadik Hayrengul³

Affiliation:

1. School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, China

2. Guangdong Provincial Key Laboratory of Computational Science and Material Design, Southern University of Science and Technology, Shenzhen 518055, China

3. College of Mathematics and Systems Science, Xinjiang University, Urumqi 830017, China

Abstract

This paper discusses the synchronization problem of impulsive stochastic bidirectional associative memory neural networks with a diffusion term, specifically focusing on the fixed-time (FXT) and predefined-time (PDT) synchronization. First, a number of more relaxed lemmas are introduced for the FXT and PDT stability of general types of impulsive nonlinear systems. A controller that does not require a sign function is then proposed to ensure that the synchronization error converges to zero within a predetermined time. The controllerdesigned in this paper serves the additional purpose of preventing the use of an unreliable inequality in the course of proving the main results. Next, to guarantee FXT and PDT synchronization of the drive–response systems, this paper employs the Lyapunov function method and derives sufficient conditions. Finally, a numerical simulation is presented to validate the theoretical results.

Funder

the Guangdong Provincial Key Laboratory of Computational Science and Material Design

Publisher

MDPI AG

Link

https://www.mdpi.com/2227-7390/12/8/1204/pdf

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