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dc.contributor.authorHsiao, Teshengen_US
dc.date.accessioned2014-12-08T15:11:05Z-
dc.date.available2014-12-08T15:11:05Z-
dc.date.issued2008-08-01en_US
dc.identifier.issn1053-587Xen_US
dc.identifier.urihttp://dx.doi.org/10.1109/TSP.2008.919393en_US
dc.identifier.urihttp://hdl.handle.net/11536/8495-
dc.description.abstractTime-varying systems and nonstationary signals arise naturally in many engineering applications, such as speech, biomedical, and seismic signal processing. Thus, identification of the time-varying parameters is of crucial importance in the analysis and synthesis of these systems. The present time-varying system identification techniques require either demanding computation power to draw a large amount of samples (Monte Carlo-based methods) or a wise selection of basis functions (basis expansion methods). In this paper, the identification of time-varying autoregressive systems is investigated. It is formulated as a Bayesian inference problem with constraints on the conditional and prior probabilities of the time-varying parameters. These constraints can be set without further knowledge about the physical system. In addition, only a few hyper parameters need tuning for better performance. Based on these probabilistic constraints, an iterative algorithm is proposed to evaluate the maximum a posteriori estimates of the parameters. The proposed method is computationally efficient since random sampling is no longer required. Simulation results show that it is able to estimate the time-varying parameters reasonably well and a balance between the bias and variance of the estimation is achieved by adjusting the hyperparameters. Moreover, simulation results indicate that the proposed method outperforms the particle filter in terms of estimation errors and computational efficiency.en_US
dc.language.isoen_USen_US
dc.subjectmaximum a posteriori estimationen_US
dc.subjecttime-varying autoregressive modelen_US
dc.subjecttime-varying system identificationen_US
dc.titleIdentification of time-varying autoregressive systems using maximum a posteriori estimationen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TSP.2008.919393en_US
dc.identifier.journalIEEE TRANSACTIONS ON SIGNAL PROCESSINGen_US
dc.citation.volume56en_US
dc.citation.issue8en_US
dc.citation.spage3497en_US
dc.citation.epage3509en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000258032800010-
dc.citation.woscount6-
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