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035   $a(OCoLC)1373985360
035   $a(DE-He213)978-981-99-0593-5
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035   $a(CKB)26291141800041
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100   $a20230320d2023      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aBayesian Real-Time System Identification $eFrom Centralized to Distributed Approach /$fby Ke Huang, Ka-Veng Yuen
205   $a1st ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (286 pages)
311 08$aPrint version: Huang, Ke Bayesian Real-Time System Identification Singapore : Springer Singapore Pte. Limited,c2023 9789819905928 
320   $aIncludes bibliographical references.
327   $aChapter 1. Introduction -- Chapter 2. System identification by Kalman filter and extended Kalman filter -- Chapter 3. Outlier detection for real-time system identification -- Chapter 4. Real-time updating of noise parameters for structural identification -- Chapter 5. Bayesian model class selection for real-time system identification -- Chapter 6. Online distributed identification for wireless sensor networks -- Chapter 7. Online distributed identification handling asynchronous data and multiple outlier-corrupted data.
330   $aThis book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchersin civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.
606   $aDynamics
606   $aNonlinear theories
606   $aStatistics
606   $aCivil engineering
606   $aApplied Dynamical Systems
606   $aBayesian Inference
606   $aCivil Engineering
615  0$aDynamics.
615  0$aNonlinear theories.
615  0$aStatistics.
615  0$aCivil engineering.
615 14$aApplied Dynamical Systems.
615 24$aBayesian Inference.
615 24$aCivil Engineering.
676   $a519.542
700   $aHuang$b Ke$01347168
702   $aYuen$b Ka-Veng
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910682562103321
996   $aBayesian real-time system identification$93419634
997   $aUNINA