Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study

Abstract The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm. In this study, several neural and neurofuzzy models with different learning algorithms are examined for p...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Gholipour, Ali [verfasserIn]

Araabi, Babak N. [verfasserIn]

Lucas, Caro [verfasserIn]

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2006

Schlagwörter:

adaptive network based fuzzy inference system

chaotic time series

locally linear models

locally linear model tree

multilayer function

neurofuzzy models

nonlinear time series

prediction

radial basis function

Übergeordnetes Werk:

Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 24(2006), 3 vom: 20. Sept., Seite 217-239

Übergeordnetes Werk:

volume:24 ; year:2006 ; number:3 ; day:20 ; month:09 ; pages:217-239

Links:

Volltext

DOI / URN:

10.1007/s11063-006-9021-x

Katalog-ID:

SPR016219430

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