{"title":"Globally Exponential Synchronization and Parameter Regulation of Chaotic Neural Networks with Time-Varying Delays via Adaptive Control","authors":"Zhongsheng Wang, Dan Xiang, Nin Yan","doi":"10.1109/ICNC.2008.32","DOIUrl":null,"url":null,"abstract":"The paper aims to present a globally exponential synchronization and parameter regulation scheme for a class of time-varying neural networks, which covers the Hopfield neural networks and cellular neural networks. By combining the adaptive control method and the Razumikhin-type theorem, a delay-independent and decentralized linear-feedback control with appropriate updated law is designed to achieve the globally exponential synchronization. The regulating law of parameters can be directly constructed. Hopfield neural networks with time-varying delays is given to show the effectiveness of the presented synchronization scheme.","PeriodicalId":6404,"journal":{"name":"2008 Fourth International Conference on Natural Computation","volume":"24 1","pages":"409-413"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Fourth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2008.32","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The paper aims to present a globally exponential synchronization and parameter regulation scheme for a class of time-varying neural networks, which covers the Hopfield neural networks and cellular neural networks. By combining the adaptive control method and the Razumikhin-type theorem, a delay-independent and decentralized linear-feedback control with appropriate updated law is designed to achieve the globally exponential synchronization. The regulating law of parameters can be directly constructed. Hopfield neural networks with time-varying delays is given to show the effectiveness of the presented synchronization scheme.
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