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Fuzzy Cellular Neural Network: a new paradigm for image processing
Author(s) -
Yang Tao,
Yang LinBao
Publication year - 1997
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(199711/12)25:6<469::aid-cta967>3.0.co;2-1
Subject(s) - cellular neural network , fuzzy logic , extension (predicate logic) , artificial neural network , transformation (genetics) , type (biology) , computer science , image processing , image (mathematics) , noise (video) , range (aeronautics) , algorithm , artificial intelligence , mathematics , engineering , ecology , biochemistry , chemistry , biology , gene , programming language , aerospace engineering
A non‐linear network structure called the fuzzy cellular neural network (FCNN) is presented. It is a reasonable extension of the cellular neural network (CNN) from classical to fuzzy sets. In this paper, structures of type II FCNNs are presented. A type II FCNN has fuzzy signals and crisp synaptic weights. Some theorems on the dynamical range and equilibrium points of type II FCNNs are presented. Applications of type II FCNNs to min‐max medical axis transformation, noise removal and edge detection under a low‐SNR condition are presented. Computer simulation results are given. © 1997 by John Wiley & Sons, Ltd.