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Template design methods for binary stable cellular neural networks
Author(s) -
Gilli Marco,
Paolo Civalleri Pier
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.197
Subject(s) - monotonic function , template , binary number , class (philosophy) , cellular neural network , simple (philosophy) , computer science , artificial neural network , sign (mathematics) , algorithm , mathematics , artificial intelligence , arithmetic , programming language , mathematical analysis , philosophy , epistemology
Stable cellular neural networks with binary outputs implement a non‐linear mapping between sets of input and output images. Such a mapping is studied in detail. We prove two theorems: the first one yields a sufficient condition in order that the non‐linear mapping be well‐defined; the second one yields a condition, that allows to describe the mapping through a simple algorithm based on the sign of the initial derivatives. Then we enunciate two additional theorems and two corollaries, that identify the class of templates satisfying the above condition: such a class is shown to be rather large and include, as particular cases, the monotonic templates, and several kinds of non‐monotonic templates. Finally,a rigorous design procedure is proposed. Copyright © 2002 John Wiley & Sons, Ltd.