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

Title: On the monotonicity of entropy for multilayer cellular neural networks
Authors: 班榮超
Ban, Jung-Chao
Chang, Chih-Hung
Contributors: 應數系
Keywords: Cellular neural networks;sofic shift;topological entropy;forcing relation;maximaltemplate;minimal template
Date: 2009-11
Issue Date: 2020-06-22 13:42:07 (UTC+8)
Abstract: This work investigates the monotonicity of topological entropy for one-dimensional multilayer cellular neural networks. The interacting radius and number of layers are treated as parameters. Fix either one of them; the set of topological entropies grows as a strictly nested sequence with respect to one another. Apart from the comparison of the set of topological entropies, maximal and minimal templates are indicators of a dynamical system. Our results demonstrate that maximal and minimal templates of larger interacting radius (respectively number of layers) dominate those of smaller one. To be precise, the strict monotonicity of topological entropy is demonstrated through the comparison of the maximal and minimal templates as the parameters are varied.
Relation: International Journal of Bifurcation and Chaos, Vol.19, No.11, pp.3657-3670
Data Type: article
DOI 連結:
Appears in Collections:[應用數學系] 期刊論文

Files in This Item:

File Description SizeFormat
108.pdf315KbAdobe PDF54View/Open

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing