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Conference Paper: Identification of non-Gaussian parametric model with time-varying coefficients using wavelet basis

TitleIdentification of non-Gaussian parametric model with time-varying coefficients using wavelet basis
Authors
Issue Date2003
PublisherIEEE.
Citation
International Conference on Neural Networks and Signal Processing Proceedings, Nanjing, China, 14-17 December 2003, v. 1, p. 659-662 How to Cite?
AbstractMany time series in practice turn to be the time-varying (TV) non-Gaussian processes. In this paper, we address the problem of how to describe these non-stationary non-Gaussian time series. A non-Gaussian AR model with TV parameters is proposed to track the non-stationary non-Gaussian characteristics of the signal. Since wavelet has flexibility in capturing the signal's transient characteristics at different scales, a set of wavelet basis is employed so that the model parameters can effectively track the variations of TV signals and be used to estimate the corresponding TV bispectrum. The experiments results confirm the superior performance of the presented model over the previous method.
Persistent Identifierhttp://hdl.handle.net/10722/46510
ISBN

 

DC FieldValueLanguage
dc.contributor.authorShen, MFen_HK
dc.contributor.authorZhang, YZen_HK
dc.contributor.authorChan, FHYen_HK
dc.date.accessioned2007-10-30T06:51:35Z-
dc.date.available2007-10-30T06:51:35Z-
dc.date.issued2003en_HK
dc.identifier.citationInternational Conference on Neural Networks and Signal Processing Proceedings, Nanjing, China, 14-17 December 2003, v. 1, p. 659-662en_HK
dc.identifier.isbn0-7803-7702-8en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46510-
dc.description.abstractMany time series in practice turn to be the time-varying (TV) non-Gaussian processes. In this paper, we address the problem of how to describe these non-stationary non-Gaussian time series. A non-Gaussian AR model with TV parameters is proposed to track the non-stationary non-Gaussian characteristics of the signal. Since wavelet has flexibility in capturing the signal's transient characteristics at different scales, a set of wavelet basis is employed so that the model parameters can effectively track the variations of TV signals and be used to estimate the corresponding TV bispectrum. The experiments results confirm the superior performance of the presented model over the previous method.en_HK
dc.format.extent195191 bytes-
dc.format.extent13817 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©2003 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleIdentification of non-Gaussian parametric model with time-varying coefficients using wavelet basisen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0-7803-7702-8&volume=1&spage=659&epage=662&date=2003&atitle=Identification+of+non-Gaussian+parametric+model+with+time-varying+coefficients+using+wavelet+basisen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICNNSP.2003.1279361en_HK
dc.identifier.scopuseid_2-s2.0-78650115083-
dc.identifier.hkuros95141-

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