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    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:[數學研究所] 期刊論文

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