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dc.contributor.authorCelka, Patricken_US
dc.date.accessioned2006-06-26en_US
dc.date.accessioned2014-04-24T02:23:36Z
dc.date.accessioned2017-03-02T01:11:14Z
dc.date.available2017-03-02T01:11:14Z
dc.date.issued2000en_US
dc.date.modified2014-04-24T02:23:36Z
dc.identifier.issn1053-587Xen_US
dc.identifier.doi10.1109/78.845945en_US
dc.identifier.urihttp://hdl.handle.net/10072/58855
dc.description.abstractThis correspondence presents analytical results and Monte Carlo simulations for the fluctuation behavior of a stochastic gradient adaptive identification scheme. This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observations. The analysis includes (1) bounds and a recursion for the misadjustment matrix and (2) algorithm mean square stability regions. A diagonal step-size matrix for the adaptive coefficients is introduced to speed up convergence. The theoretical predictions of the fluctuation analysis are supported by Monte Carlo simulations.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.publisherIEEEen_US
dc.publisher.placeNew Yorken_US
dc.relation.ispartofpagefrom1676en_US
dc.relation.ispartofpageto1686en_US
dc.relation.ispartofjournalIEEE Transaction on Signal Processingen_US
dc.relation.ispartofvolume48en_US
dc.subject.fieldofresearchPRE2009-Signal Processingen_US
dc.subject.fieldofresearchcode280204en_US
dc.titleFluctuation analysis of stochastic gradient identification of polynomial Wiener systemsen_US
dc.typeJournal article
dc.type.descriptionArticle in Scholarly Refereed Journalen_US
dc.type.codec1xen_US
gro.facultyFaculty of Engineering and Information Technologyen_US
gro.date.issued2000
gro.hasfulltextNo Full Text
gro.griffith.authorCelka, Patrick


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