Title: Prescribed-time bipartite synchronisation of switched coupled neural networks via switching controllers

Authors: Meng Tao; Xiaoyang Liu

Addresses: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China ' School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China

Abstract: This paper is concerned with the prescribed-time bipartite synchronisation of switched coupled neural networks in signed graphs. A novel switching control method is proposed to force the coupled system into a prespecified attraction domain within a predefined time. Then, a twist controller is designed to further drive the system to its origin at another prescribed time. The dwell time of the prescribed-time control is allowed to be flexibly set based on specific task requirements, which adaptability enhances the applicability of the approach across various scenarios. The switched network topology encompasses both competitive and cooperative relationships. Finally, a simulation example is constructed to justify the theoretical results.

Keywords: bipartite synchronisation; coupled neural networks; prescribed-time control; switching controllers; signed graphs.

DOI: 10.1504/IJSCIP.2024.138670

International Journal of System Control and Information Processing, 2024 Vol.4 No.2, pp.138 - 153

Received: 29 Mar 2023
Accepted: 16 Aug 2023
Published online: 23 May 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article