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Please use this identifier to cite or link to this item: https://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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