Bayesian model order selection for nonlinear system function expansions

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dc.contributor.author Mitsis, GD en
dc.contributor.author Jbabdi, S en
dc.date.accessioned 2014-03-01T02:45:10Z
dc.date.available 2014-03-01T02:45:10Z
dc.date.issued 2008 en
dc.identifier.issn 1557170X en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/32175
dc.subject bayesian framework en
dc.subject bayesian model en
dc.subject Exponential Decay en
dc.subject laguerre function en
dc.subject Model Order Selection en
dc.subject Nonlinear System en
dc.subject Nonlinear System Identification en
dc.subject Order Selection en
dc.subject Posterior Distribution en
dc.subject Prediction Error en
dc.subject System Identification en
dc.subject.other Bayesian networks en
dc.subject.other Expansion en
dc.subject.other Neural networks en
dc.subject.other Nonlinear systems en
dc.subject.other Bayesian frameworks en
dc.subject.other Bayesian models en
dc.subject.other Exponential decays en
dc.subject.other Free parameters en
dc.subject.other Laguerre en
dc.subject.other Laguerre basis en
dc.subject.other Laguerre expansion techniques en
dc.subject.other Laguerre functions en
dc.subject.other Model predictions en
dc.subject.other Model-order selections en
dc.subject.other Non linearities en
dc.subject.other Nonlinear systems identifications en
dc.subject.other Orthonormal basis en
dc.subject.other Orthonormal functions en
dc.subject.other Physiological systems en
dc.subject.other Polynomial models en
dc.subject.other Posterior distributions en
dc.subject.other Statistical criterion en
dc.subject.other Structural parameters en
dc.subject.other System functions en
dc.subject.other Trial-and-error procedures en
dc.subject.other Volterra kernels en
dc.subject.other Identification (control systems) en
dc.title Bayesian model order selection for nonlinear system function expansions en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IEMBS.2008.4649623 en
heal.identifier.secondary http://dx.doi.org/10.1109/IEMBS.2008.4649623 en
heal.identifier.secondary 4649623 en
heal.publicationDate 2008 en
heal.abstract Orthonormal function expansions have been used extensively in the context of linear and nonlinear systems identification, since they result in a significant reduction in the number of required free parameters. In particular, Laguerre basis expansions of Volterra kernels have been used successfully for physiological systems identification, due to the exponential decaying characteristics of the Laguerre orthonormal basis and the inherent nonlinearities that characterize such systems. A critical aspect of the Laguerre expansion technique is the selection of the model structural parameters, i.e., polynomial model order, number of Laguerre functions in the expansion and value of the Laguerre parameter α, which determines the rate of exponential decay. This selection is typically made by trial-and-error procedures on the basis of the model prediction error. In the present paper, we formulate the Laguerre expansion technique in a Bayesian framework and derive analytically the posterior distribution of the a parameter, as well as the model evidence, in order to infer on the expansion structural parameters. We also demonstrate the performance of the proposed method by simulated examples and compare it to alternative statistical criteria for model order selection. © 2008 IEEE. en
heal.journalName Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - ""Personalized Healthcare through Technology"" en
dc.identifier.doi 10.1109/IEMBS.2008.4649623 en
dc.identifier.volume 2008 en
dc.identifier.spage 2165 en
dc.identifier.epage 2168 en

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