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Titolo:
EMBEDDING DIMENSION ESTIMATION OF CHAOTIC TIME-SERIES USING SELF-GENERATING RADIAL BASIS FUNCTION NETWORK
Autore:
KATAYAMA R; KUWATA K; KAJITANI Y; WATANABE M;
Indirizzi:
SANYO ELECT CO LTD,HYPERMEDIA RES CTR,1-18-13 HASHIRIDANI HIRAKATA OSAKA 573 JAPAN
Titolo Testata:
Fuzzy sets and systems
fascicolo: 3, volume: 71, anno: 1995,
pagine: 311 - 327
SICI:
0165-0114(1995)71:3<311:EDEOCT>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Soggetto:
STRANGE ATTRACTORS; APPROXIMATION; RECONSTRUCTION;
Keywords:
RADIAL BASIS FUNCTION; NEURO FUZZY MODEL; CHAOTIC TIME SERIES; FUZZY MODEL OF CLASS C-INFINITY; EMBEDDING DIMENSION ESTIMATION; MAXIMUM ABSOLUTE ERROR SELECTION METHOD; SELF-GENERATING METHOD; NONLINEAR PREDICTION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
CompuMath Citation Index
CompuMath Citation Index
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
53
Recensione:
Indirizzi per estratti:
Citazione:
R. Katayama et al., "EMBEDDING DIMENSION ESTIMATION OF CHAOTIC TIME-SERIES USING SELF-GENERATING RADIAL BASIS FUNCTION NETWORK", Fuzzy sets and systems, 71(3), 1995, pp. 311-327

Abstract

In this paper, we apply the self-generating radial basis function network (SGRBF) to the dimension analysis of the nonlinear dynamical systems including chaotic time series. Firstly, we formulate a nonlinear time series identification problem with a nonlinear autoregressive moving average (NARMAX) model. Secondly, we propose an identification algorithm using SGRBF, which is regarded as both a three-layer network or a fuzzy model of class C-infinity with Gaussian membership function. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of nonlinear dynamical systems including chaos. In this estimation method, identification problemswith gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the embedding dimension estimation of a Henon map and a chaotic pulsation time series in a finger's capillary vessels.

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Documento generato il 30/09/20 alle ore 02:23:48