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On the rectangular grid representation of general CNN networks
Author(s) -
Radványi András G.
Publication year - 2002
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.195
Subject(s) - grid , computer science , cellular neural network , representation (politics) , artificial neural network , space (punctuation) , theoretical computer science , topology (electrical circuits) , algorithm , mathematics , artificial intelligence , geometry , combinatorics , politics , political science , law , operating system
Although the cellular neural paradigm in its original form provides a suitable framework for investigating problems defined on arbitrary regular grids, the chips—ready ones or under design—as well as the available simulators are all restricted to a rectangular structure. It is not at all self‐evident, however, that the rectangular structure is the most suitable to represent every practical problem. In this paper we demonstrate that several cellular neural networks of various regular grids can be mapped onto the typical eight‐neighbour rectangular one, by applying weight matrices of periodic space variance. By adopting this option, the applicability of cellular neural chips and simulators can be extended to investigate and solve problems of essentially arbitrary grid structures. Copyright © 2002 John Wiley & Sons, Ltd.