Please use this identifier to cite or link to this item: https://ah.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
Appears in Collections:[應用數學系] 期刊論文

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