Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

Author:

Fan Hongguang123ORCID,Tang Jiahui12,Shi Kaibo4ORCID,Zhao Yi5

Affiliation:

1. College of Computer, Chengdu University, Chengdu 610106, China

2. Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu University, Chengdu 610106, China

3. School of Mathematical and Computational Science, Hunan University of Science and Technology, Xiangtan 411201, China

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

5. College of Mathematical and Statistical, Shenzhen University, Shenzhen 518060, China

Abstract

This article addresses the issue of drive–response synchronization in fractional-order multi-link memristive neural networks (FMMNN) with multiple delays, under hybrid impulsive feedback control. To address the impact of multiple delays on system synchronization, an extended fractional-order delayed comparison principle incorporating impulses is established. By leveraging Laplace transform, Mittag–Leffler functions, the generalized comparison principle, and hybrid impulsive feedback control schemes, several new sufficient conditions are derived to ensure synchronization in the addressed FMMNN. Unlike existing studies on fractional-order single-link memristor-based systems, our response network is a multi-link model that considers impulsive effects. Notably, the impulsive gains αi are not limited to a small interval, thus expanding the application range of our approach (αi∈(−2,0)∪(−∞,−2)∪(0,+∞)). This feature allows one to choose impulsive gains and corresponding impulsive intervals that are appropriate for the system environment and control requirements. The theoretical results obtained in this study contribute to expanding the relevant theoretical achievements of fractional-order neural networks incorporating memristive characteristics.

Funder

Open Fund Project of Pattern Recognition and Intelligent Information Processing Laboratory

Sichuan Science and Technology Program

Program of Science and Technology of Sichuan Province of China

National Natural Science Foundation of China

GuangDong Basic and Applied Basic Research Foundation of China

Publisher

MDPI AG

Subject

Statistics and Probability,Statistical and Nonlinear Physics,Analysis

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