{"title":"Chaos in the discrete time cellular neural networks","authors":"Chunmei Yang, Ta-lun Yang, Kangming Zhang","doi":"10.1109/CNNA.1994.381663","DOIUrl":null,"url":null,"abstract":"The quasi-period and chaos in discrete time cellular neural networks (DTCNN) are studied in this paper. In a 2-cell autonomous DTCNN, theories for periodic and quasi-periodic motions are presented. Chaos is found in 2 and 3-cell autonomous and nonautonomous DTCNNs. The structures of the strange attractors are shown. The bifurcation diagrams are used to show the transition procedures of the DTCNNs from the periodic motion to chaos. A strange attractor with 2 separated branches is also found in a 3-cell DTCNN.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1994.381663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The quasi-period and chaos in discrete time cellular neural networks (DTCNN) are studied in this paper. In a 2-cell autonomous DTCNN, theories for periodic and quasi-periodic motions are presented. Chaos is found in 2 and 3-cell autonomous and nonautonomous DTCNNs. The structures of the strange attractors are shown. The bifurcation diagrams are used to show the transition procedures of the DTCNNs from the periodic motion to chaos. A strange attractor with 2 separated branches is also found in a 3-cell DTCNN.<>