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Titre du document / Document title

A recurrent self-organizing neural fuzzy inference network

Auteur(s) / Author(s)

JUANG C.-F. (1) ; LIN C.-T. (1) ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

(1) Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, TAIWAN, PROVINCE DE CHINE

Résumé / Abstract

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly, Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Revue / Journal Title

IEEE transactions on neural networks    ISSN  1045-9227   CODEN ITNNEP 

Source / Source

1999, vol. 10, no4, pp. 828-845 (52 ref.)

Langue / Language

Anglais

Editeur / Publisher

Institute of Electrical and Electronics Engineers, New York, NY, ETATS-UNIS  (1990-2011) (Revue)

Mots-clés anglais / English Keywords

Inference

;

Fuzzy system

;

Feedback system

;

Nodes

;

Bypass

;

Projection method

;

Correlation method

;

Neural network

;

Theoretical study

;

Mots-clés français / French Keywords

Inférence

;

Système flou

;

Système asservi

;

Noeud structure

;

Dérivation

;

Méthode projection

;

Méthode corrélation

;

Réseau neuronal

;

Etude théorique

;

Mots-clés espagnols / Spanish Keywords

Inferencia

;

Sistema difuso

;

Servomecanismo

;

Nudo estructura

;

Derivación

;

Método proyección

;

Método correlación

;

Red neuronal

;

Estudio teórico

;

Localisation / Location

INIST-CNRS, Cote INIST : 22204, 35400008581432.0090

Nº notice refdoc (ud4) : 1876146



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