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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


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    題名: Spatial complexity in multi-layer cellular neural networks
    作者: 班榮超
    Ban, Jung-Chao
    Chang, Chih-Hung
    Lin, Song-Sun
    Lin, Yin-Heng
    貢獻者: 應數系
    關鍵詞: Learning problem
    日期: 2009-01
    上傳時間: 2020-06-22 13:43:25 (UTC+8)
    摘要: 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.
    關聯: Journal of Differential Equations, Vol.246, No.2, pp.552-580
    資料類型: article
    DOI 連結: http://dx.doi.org/10.1109/CNNA.2010.5430257
    DOI: 10.1109/CNNA.2010.5430257
    顯示於類別:[應用數學系] 期刊論文

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