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Conference Paper: Modelling of nonlinear stochastic dynamical systems using neurofuzzy networks

TitleModelling of nonlinear stochastic dynamical systems using neurofuzzy networks
Authors
Keywordsstochastic dynamical system
NARMAX model
neurofuzzy network
Newton-Raphson method
Issue Date1999
PublisherIEEE. The Journal's web site is located at http://www.ieeecss.org
Citation
Proceedings Of The Ieee Conference On Decision And Control, 1999, v. 3, p. 2643-2648 How to Cite?
AbstractThough nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in practical application. It is well known that the training of recurrent networks using gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.
Persistent Identifierhttp://hdl.handle.net/10722/46653
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChan, WCen_HK
dc.contributor.authorChan, CWen_HK
dc.contributor.authorCheung, KCen_HK
dc.contributor.authorWang, Yen_HK
dc.date.accessioned2007-10-30T06:55:09Z-
dc.date.available2007-10-30T06:55:09Z-
dc.date.issued1999en_HK
dc.identifier.citationProceedings Of The Ieee Conference On Decision And Control, 1999, v. 3, p. 2643-2648en_HK
dc.identifier.issn0191-2216en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46653-
dc.description.abstractThough nonlinear stochastic dynamical system can be approximated by feedforward neural networks, the dimension of the input space of the network may be too large, making it to be of little practical importance. The Nonlinear Autoregressive Moving Average model with eXogenous input (NARMAX) is shown to be able to represent nonlinear stochastic dynamical system under certain conditions. As the dimension of the input space is finite, it can be readily applied in practical application. It is well known that the training of recurrent networks using gradient method has a slow convergence rate. In this paper, a fast training algorithm based on the Newton-Raphson method for recurrent neurofuzzy network with NARMAX structure is presented. The convergence and the uniqueness of the proposed training algorithm are established. A simulation example involving a nonlinear dynamical system corrupted with the correlated noise and a sinusoidal disturbance is used to illustrate the performance of the proposed training algorithm.en_HK
dc.format.extent493584 bytes-
dc.format.extent5145 bytes-
dc.format.extent3469 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE. The Journal's web site is located at http://www.ieeecss.orgen_HK
dc.relation.ispartofProceedings of the IEEE Conference on Decision and Controlen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectstochastic dynamical systemen_HK
dc.subjectNARMAX modelen_HK
dc.subjectneurofuzzy networken_HK
dc.subjectNewton-Raphson methoden_HK
dc.titleModelling of nonlinear stochastic dynamical systems using neurofuzzy networksen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0743-1546&volume=3&spage=2643&epage=2648&date=1999&atitle=Modelling+of+nonlinear+stochastic+dynamical+systems+using+neurofuzzy+networksen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.emailCheung, KC: kccheung@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.identifier.authorityCheung, KC=rp01322en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/CDC.1999.831328en_HK
dc.identifier.scopuseid_2-s2.0-0033312281en_HK
dc.identifier.hkuros49556-
dc.identifier.volume3en_HK
dc.identifier.spage2643en_HK
dc.identifier.epage2648en_HK
dc.identifier.scopusauthoridChan, WC=36503653500en_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK
dc.identifier.scopusauthoridCheung, KC=7402406698en_HK
dc.identifier.scopusauthoridWang, Y=7601487533en_HK

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