{"title":"Chaos synchronization in coupled SC-CNN based variant of MLC circuit: An analytical study","authors":"H. Banu, P. S. Sheik Uduman","doi":"10.1109/RTECC.2018.8625654","DOIUrl":null,"url":null,"abstract":"In this paper, we presented an explicitly analytical solution of complete synchronization in a State Controlled Cellular Neural Network (SC-CNN) based Variant of Murali-Lakshmanan-Chua (MLCV) circuit. For this the two individual systems are unidirectionally coupled with each other. The system transit from the unsynchronized state to the complete synchronization state under the influence of coupling parameter. we demonstrate the synchronized state of the model system using analytically obtained temporal state of the drive and response systems as well as their corresponding phase portraits.","PeriodicalId":445688,"journal":{"name":"2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RTECC.2018.8625654","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
In this paper, we presented an explicitly analytical solution of complete synchronization in a State Controlled Cellular Neural Network (SC-CNN) based Variant of Murali-Lakshmanan-Chua (MLCV) circuit. For this the two individual systems are unidirectionally coupled with each other. The system transit from the unsynchronized state to the complete synchronization state under the influence of coupling parameter. we demonstrate the synchronized state of the model system using analytically obtained temporal state of the drive and response systems as well as their corresponding phase portraits.