標題: ON THE DENSE ENTROPY OF TWO-DIMENSIONAL INHOMOGENEOUS CELLULAR NEURAL NETWORKS
作者: Ban, Jung-Chao
Chang, Chih-Hung
應用數學系
Department of Applied Mathematics
關鍵字: Entropy;learning problem;ICNN
公開日期: 1-Nov-2008
摘要: 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.
URI: http://dx.doi.org/10.1142/S0218127408022378
http://hdl.handle.net/11536/8228
ISSN: 0218-1274
DOI: 10.1142/S0218127408022378
期刊: INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
Volume: 18
Issue: 11
起始頁: 3221
結束頁: 3231
Appears in Collections:Articles


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