NDLI: Bayesian estimation of dynamic systems function expansions

Content Provider	IEEE Xplore Digital Library
Author	Mitsis, G.D. Jbabdi, S.
Copyright Year	2010
Description	Author affiliation: Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, OX3 9DU, United Kingdom (Jbabdi, S.) \|\| Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus (Mitsis, G.D.)
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 have been used in the context of biological/physiological systems identification, due to the exponential decaying characteristics of the Laguerre orthonormal basis, the rate of which is determined by the Laguerre parameter α. A critical aspect of the Laguerre expansion technique is the selection of the model structural parameters, i.e., polynomial model order for nonlinear systems, number of Laguerre functions and value of the Laguerre parameter α. 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. Based on this formulation, we derive analytically the posterior distribution of the α parameter and the model evidence, in order to perform model order selection. We also demonstrate the performance of the proposed method by simulated examples and compare it to alternative statistical criteria for model order selection.
Starting Page	1
Ending Page	5
File Size	5810556
Page Count	5
File Format	PDF
ISBN	9781424462858
e-ISBN	9781424462872
DOI	10.1109/ISCCSP.2010.5463426
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-03
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear systems Bayesian methods Biological system modeling Predictive models Nonlinear control systems Polynomials Structural engineering Nonlinear systems Kernel Context modeling
Content Type	Text
Resource Type	Article

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