Unification of neural and wavelet networks and fuzzy systems

Author

Reyneri, Leonardo M.

Author_Institution

Dipt. di Elettronica, Politecnico di Torino, Italy

Volume

10

Issue

4

fYear

1999

fDate

7/1/1999 12:00:00 AM

Firstpage

801

Lastpage

814

Abstract

Analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other. These are exploited to produce the weighted radial basis functions paradigm which may act as a neuro-fuzzy unification paradigm. Training rules (both supervised and unsupervised) are also unified by the proposed algorithm. Analyzing differences and similarities among existing paradigms helps to understand that many soft computing paradigms are very similar to each other and can be grouped in just two major classes. The many reasons to unify soft computing paradigms are also shown in the paper. A conversion method is presented to convert perceptrons, radial basis functions, wavelet networks, and fuzzy systems from each other

Keywords

Bayes methods; function approximation; fuzzy systems; pattern classification; perceptrons; radial basis function networks; unsupervised learning; Bayesian classifiers; fuzzy partitions; neuro-fuzzy unification paradigm; perceptrons; soft computing paradigms; supervised learning; wavelet networks; weighted radial basis functions paradigm; Artificial neural networks; Bayesian methods; Computer networks; Function approximation; Fuzzy neural networks; Fuzzy systems; Learning; Partitioning algorithms; Taxonomy; Wavelet analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.774224

Filename

774224