Work

Resilient memory sampled-data controller for synchronization of semi-Markovian jump competitive neural networks with mixed delays

Year: 2025

Type: article

Abstract: The primary aim of this paper is to design a resilient controller to ensure the synchronization of semi-Markovian jump competitive neural networks with mixed delays, which include discrete time-varying delay, leakage delay and distributed time-varying delay. To facilitate this, two-sided looped-type Lyapunov–Krasovskii functionals (LKFs) are utilized as a systematic approach for analyzing the synchronization of the system. By applying an innovative integral inequality technique based on free weighting matrices, this study establishes sufficient conditions for ensuring synchronization. A central contribution of this work is the development and implementation of a resilient memory sampled-data control mechanism, that is essential for achieving synchronization of the proposed system. This controller effectively mitigates the impact of input delays, sampling errors and external disturbances, ensuring robust performance even in the presence of uncertainties. The synchronization criteria are derived in terms of linear matrix inequalities (LMIs). The proposed method is verified through numerical simulations and the outcomes are graphically illustrated, demonstrating the efficacy of the resilient memory sampled-data controller.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.110982

Authors: A. Chandrasekar, T. Radhika, A.R. Subhashri, Hijaz Ahmad, Taha Radwan

Language: English

Topic: Neural Networks Stability and Synchronization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,920

Funder: Qassim University

Award: G92618698