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Titre du document / Document title

The non-stationary signal prediction by using quantum NN

Auteur(s) / Author(s)

LEE Chang-Der (1) ; CHEN Yu-Ju (2) ; HUANE Huane-Chu (3) ; HWANG Rey-Chue (1) ; YU Gwo-Ruey (1) ;

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

(1) Electrical Engineering Department I-Shou University, Kaohsiung, 840, TAIWAN, PROVINCE DE CHINE
(2) Industrial Engineering Management Department Cheng Shiu University, Kaohsiung, 833, TAIWAN, PROVINCE DE CHINE
(3) Electric Communication Department National Kaohsiung Marine University, Kaohsiung, 814, TAIWAN, PROVINCE DE CHINE

Résumé / Abstract

In this paper, the non-stationary power signal prediction by using quantum neural network (QNN) is proposed. The signals with fuzziness are expected to be classified clearly for enhancing the learning efficiency of neural network due to the hidden units with various graded levels in QNN structure. For a comparison, all experiments are also performed by using the conventional neural network (CNN) structure.

Source / Source

Congrès
2004 IEEE international conference on systems, man & cybernetics :   ( The Hague, Netherlands, 10-13 october 2004 )
International Conference on Systems, Man and Cybernetics, The Hague , PAYS-BAS (10/10/2004)
2004  [Note(s) : 7 vol., ] (17 ref.) ISBN 0-7803-8566-7 ;  Illustration : Illustration ;

Langue / Language

Anglais

Editeur / Publisher

IEEE, Piscataway NJ, ETATS-UNIS  (2004) (Monographie)

Mots-clés anglais / English Keywords

Neural network

;

Stationary signal

;

Non stationary condition

;

Non stationary process

;

Mots-clés français / French Keywords

Réseau neuronal

;

Signal stationnaire

;

Condition non stationnaire

;

Processus non stationnaire

;

Mots-clés espagnols / Spanish Keywords

Red neuronal

;

Señal estacionaria

;

Condición no estacionaria

;

Proceso no estacionario

;

Localisation / Location

INIST-CNRS, Cote INIST : y 38703(1), 35400013871166.2765

Nº notice refdoc (ud4) : 17523948



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