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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:
Appears in Collections:[應用數學系] 期刊論文

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