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    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/130204
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/130204


    Title: Spatial complexity in multi-layer cellular neural networks
    Authors: 班榮超
    Ban, Jung-Chao
    Chang, Chih-Hung
    Lin, Song-Sun
    Lin, Yin-Heng
    Contributors: 應數系
    Keywords: Learning problem
    Date: 2009-01
    Issue Date: 2020-06-22 13:43:25 (UTC+8)
    Abstract: This study investigates the complexity of the global set of output patterns for one-dimensional multi-layer cellular neural networks with input. Applying labeling to the output space produces a sofic shift space. Two invariants, namely spatial entropy and dynamical zeta function, can be exactly computed by studying the induced sofic shift space. This study gives sofic shift a realization through a realistic model. Furthermore, a new phenomenon, the broken of symmetry of entropy, is discovered in multi-layer cellular neural networks with input.
    Relation: Journal of Differential Equations, Vol.246, No.2, pp.552-580
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1109/CNNA.2010.5430257
    DOI: 10.1109/CNNA.2010.5430257
    Appears in Collections:[應用數學系] 期刊論文

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