An ESETM based robust synchronizing control on master-slave neural network with multiple time-varying delays. Issue 13 (September 2022)

• Record Type: Journal Article
• Authors: Long, Shengsheng; Zhang, Zhenzhen; Chen, Hao; Zhang, Pengyu; Wang, Huazhang; Zhong, Shouming
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 13(2022)
• Page Start: 6632
• Page End: 6658
• Publication Date: 2022-09
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2022.07.017
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper focuses on the synchronization issue of master-slave neural network with multiple time-varying delays by way of an enhanced stretchy event-triggered mechanism (ESETM). Firstly, multiple time-varying delays are considered to express the generalization and complexity of system behavior. Secondly, the ESETM is designed by employing an internal dynamic variable and a modified adaptive function, which can provide the expected robustness performance during synchronization on the premise of saving limited network resources. Thirdly, less conservative stabilization criteria are derived in terms of an asymmetric Lyapunov-Krasovskii functional (LKF) candidate. In addition, Wirtinger-based integral inequality and reciprocally convex combination are utilized to estimate derivative of the constructed LKF. Meanwhile, a general activation function method is developed to introduce more information of system states on cross terms of neuron activation function to further reduce the conservatism. Finally, two numerical examples are illustrated to demonstrate the merits and effectiveness of the development.