Work

Predefined-Time Synchronization of Fractional-Order Delayed Memristor-Based Fuzzy Cellular Neural Networks

Year: 2026

Type: article

Abstract: In contrast to finite-time and fixed-time synchronization, predefined-time synchronization allows for the convergence time to be explicitly defined as a deterministic value, making it ideal for applications that have stringent timing requirements. This paper addresses the predefined-time synchronization of fractional-order delayed memristive fuzzy cellular neural networks (FDMFCNNs). By leveraging the properties inherent in fractional calculus, we introduce a novel lemma. Based on this lemma and a discontinuous state feedback control method, we derive several innovative criteria for achieving predefined-time synchronization in FDMFCNNs by constructing an appropriate Lyapunov function. The findings presented in this study significantly enhance and broaden the existing literature. Finally, we validate the efficacy of our control strategies for predefined-time synchronization with the help of numerical simulations.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract10100719

Authors: Yeguo Sun, Yinyin Li, Yihong Liu, Lei Liu

Institution: Huainan Normal University

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: $3,258

Funders: National Natural Science Foundation of China, Anhui Provincial Department of Education