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    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/130197
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/130197

    Title: On the dense entropy of two-dimensional inhomogeneous cellular neural networks
    Authors: 班榮超
    Ban, Jung-Chao
    Chang, Chih-Hung
    Contributors: 應數系
    Keywords: Entropy;learning problem;ICNN
    Date: 2008-11
    Issue Date: 2020-06-22 13:41:53 (UTC+8)
    Abstract: This investigation elucidates the dense entropy of two-dimensional inhomogeneous cellular neural networks (ICNN) with/without input. It is strongly related to the learning problem (or inverse problem); the necessary and sufficient conditions for the admissibility of local patterns must be characterized. For ICNN with/without input, the entropy function is dense in [0, log 2] with respect to the parameter space and the radius of the interacting cells, indicating that, in some sense, ICNN exhibit a wide range of phenomena.
    Relation: International Journal of Bifurcation and Chaos, Vol.18, No.11, pp.3221-3231
    Data Type: article
    DOI 連結: https://doi.org/10.1142/S0218127408022378
    DOI: 10.1142/S0218127408022378
    Appears in Collections:[應用數學系] 期刊論文

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