{"title":"Finite-time synchronization of delayed chaotic neural networks based on event-triggered intermittent control","authors":"Zeyu Ruan, Junhao Hu, Jun Mei","doi":"10.1109/ICCSS53909.2021.9722021","DOIUrl":null,"url":null,"abstract":"This paper investigates the finite-time synchronization (FETS) issue for a class of chaotic neural networks with time delays via event-triggered intermittent control. The event-triggered intermittent controller, in which intermittent instants are not predesigned, is explored to achieve FETS for delayed chaotic neural networks (DCNNs). By utilizing finite-time theory and constructing Lyapunov functional, several sufficient conditions for FETS are obtained under the designed control scheme. Meanwhile, the Zeno behavior is excluded. Our results about FETS criterion are new and valid, and enrich some of the existing results. In the end, numerical simulation verifies the effectiveness of the theoretical analysis.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSS53909.2021.9722021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates the finite-time synchronization (FETS) issue for a class of chaotic neural networks with time delays via event-triggered intermittent control. The event-triggered intermittent controller, in which intermittent instants are not predesigned, is explored to achieve FETS for delayed chaotic neural networks (DCNNs). By utilizing finite-time theory and constructing Lyapunov functional, several sufficient conditions for FETS are obtained under the designed control scheme. Meanwhile, the Zeno behavior is excluded. Our results about FETS criterion are new and valid, and enrich some of the existing results. In the end, numerical simulation verifies the effectiveness of the theoretical analysis.
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