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DESIGN AND LEARNING WITH CELLULAR NEURAL NETWORKS
Author(s) -
NOSSEK JOSEF A.
Publication year - 1996
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/(sici)1097-007x(199601/02)24:1<15::aid-cta900>3.0.co;2-5
Subject(s) - computer science , set (abstract data type) , point (geometry) , artificial neural network , artificial intelligence , cellular neural network , function (biology) , mathematics , geometry , evolutionary biology , biology , programming language
The template coefficients (weights) of a CNN which will give a desired performance, can either be found by design or by learning. ’By design‘ means that the desired function to be performed can be translated into a set of local dynamic rules, while ’by learning‘ is based exclusively on pairs of input and corresponding output signals, the relationship of which may be far too complicated for the explicit formulation of local rules. An overview of design and learning methods applicable to CNNs, which sometimes are not clearly distinguishable, will be given from an engineering point of view.