Spatial complexity in multi-layer cellular neural networks

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27960

Title:	Spatial complexity in multi-layer cellular neural networks
Authors:	Ban,JC;Chang,CH;Lin,SS;Lin,YH
Contributors:	數學研究所
Keywords:	COUPLED NONLINEAR OSCILLATORS;PATTERNS GENERATION;LATTICE MODELS;DISCRETE;PROPAGATION;SYSTEMS;CHAINS;CHAOS
Date:	2009
Issue Date:	2010-06-29 19:38:56 (UTC+8)
Publisher:	中央大學
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. (C) 2008 Elsevier Inc. All rights reserved.
Relation:	JOURNAL OF DIFFERENTIAL EQUATIONS
Appears in Collections:	[Graduate Institute of Mathematics] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	502	View/Open

社群 sharing