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Event-triggered protocol-based adaptive impulsive control for delayed chaotic neural networks

Title: Event-triggered protocol-based adaptive impulsive control for delayed chaotic neural networks
Authors: Diao, Weilu; He, Wangli
Contributors: National Natural Science Foundation of China (Basic Science Center Program; Shanghai International Science and Technology Cooperation Program; Shanghai Pilot Program for Basic Research; Joint Fund of Ministry of Education for Equipment Pre-research; Fundamental Research Funds for the Central Universities and Shanghai AI Lab
Source: Chaos: An Interdisciplinary Journal of Nonlinear Science ; volume 34, issue 6 ; ISSN 1054-1500 1089-7682
Publisher Information: AIP Publishing
Publication Year: 2024
Description: This article focuses on the synchronization problem of delayed chaotic neural networks via adaptive impulsive control. An adaptive impulsive gain law in a discrete-time framework is designed. The delay is handled skillfully by using the Lyapunov–Razumikhin method. To improve the flexibility of impulsive control, an event-triggered impulsive strategy to determine when the impulsive instant happens is designed. Additionally, it is proved that the event-triggered impulsive sequence cannot result in the occurrence of Zeno behavior. Some criteria are derived to guarantee synchronization for delayed chaotic neural networks. Eventually, an illustrative example is presented to empirically validate the effectiveness of the suggested strategy.
Document Type: article in journal/newspaper
Language: English
DOI: 10.1063/5.0211621
DOI: 10.1063/5.0211621/19993445/063132_1_5.0211621.pdf
Availability: https://doi.org/10.1063/5.0211621; https://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/5.0211621/19993445/063132_1_5.0211621.pdf
Accession Number: edsbas.EFE67D08
Database: BASE