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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2015

01.08.2015 | Original Article

Neural network equations and symbolic dynamics

verfasst von: Jung-Chao Ban

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2015

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Abstract

In this paper we provide an up-to-date survey on the study of the complexity of the mosaic solutions on neural network equations. Three types of equations, namely, cellular neural networks (CNNs), multi-layer CNN (MCNNs) and inhomogeneous CNNs (ICNNs) are discuss herein. Such topic strong related to the learning algorithm and training process on neural network equations. Each neural network produces different mosaic solution space, and each mosaic solution space induces an different symbolic dynamics. To understand the complexity (spatial entropy) of the mosaic solution space for a given neural network equation, we need to identify which the underlying symbolic space is, then using the established knowledge of symbolic dynamical systems to compute its spatial entropy. Recently there has been substantial progress in this field. This paper is a comprehensive survey of this field. It provides a summary of the interesting results in this field. It is our hope that the paper will provide a good overview of major results and techniques, and a friendly entry point for anyone who is interested in studying problems in this field.
Vorheriger Artikel New results of stability analysis for a class of neutral-type neural network with mixed time delays
Nächster Artikel Efficient structured tracker based on laplacian error distribution

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1
Such coupling is suggested by Chua and Roska. The purpose is to design MCNNs for solving some image processing and pattern recognition problems [25].
 
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Metadaten
Titel
Neural network equations and symbolic dynamics
verfasst von
Jung-Chao Ban
Publikationsdatum
01.08.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0244-2

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