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題名 Inhomogeneous lattice dynamical systems and the boundary effect
作者 班榮超
Ban, Jung-Chao
Chang, Chih-Hung
貢獻者 應數系
關鍵詞 inhomogeneous lattice dynamical systems ; topological entropy ; boundary value problem ; multiplicative shift spaces ; cellular neural networks
日期 2013-12
上傳時間 22-Jun-2020 13:41:28 (UTC+8)
摘要 This study considers the dynamics of cellular neural network-based inhomogeneous lattice dynamical systems (CNN-based ILDS). The influence of three kinds of boundary conditions, say, the periodic, Dirichlet, and Neumann boundary conditions, is elucidated. We reveal that the complete stability of CNN-based ILDS and, under some prescriptions, the topological entropies of CNN-based ILDS with/without the boundary condition are identical.
關聯 Boundary Value Problems, Vol.2013, No.1, pp.249
資料類型 article
DOI http://dx.doi.org/10.1186/1687-2770-2013-249
dc.contributor 應數系-
dc.creator (作者) 班榮超-
dc.creator (作者) Ban, Jung-Chao-
dc.creator (作者) Chang, Chih-Hung-
dc.date (日期) 2013-12-
dc.date.accessioned 22-Jun-2020 13:41:28 (UTC+8)-
dc.date.available 22-Jun-2020 13:41:28 (UTC+8)-
dc.date.issued (上傳時間) 22-Jun-2020 13:41:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130194-
dc.description.abstract (摘要) This study considers the dynamics of cellular neural network-based inhomogeneous lattice dynamical systems (CNN-based ILDS). The influence of three kinds of boundary conditions, say, the periodic, Dirichlet, and Neumann boundary conditions, is elucidated. We reveal that the complete stability of CNN-based ILDS and, under some prescriptions, the topological entropies of CNN-based ILDS with/without the boundary condition are identical.-
dc.format.extent 501685 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Boundary Value Problems, Vol.2013, No.1, pp.249-
dc.subject (關鍵詞) inhomogeneous lattice dynamical systems ; topological entropy ; boundary value problem ; multiplicative shift spaces ; cellular neural networks-
dc.title (題名) Inhomogeneous lattice dynamical systems and the boundary effect-
dc.type (資料類型) article-
dc.identifier.doi (DOI) 10.1186/1687-2770-2013-249-
dc.doi.uri (DOI) http://dx.doi.org/10.1186/1687-2770-2013-249-