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Bayesian real-time system identificationfrom centralized to distributed approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian real-time system identificationby Ke Huang, Ka-Veng Yuen.
Reminder of title:	from centralized to distributed approach /
Author:	Huang, Ke.
other author:	Yuen, Ka-Veng.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xii, 276 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	System identificationData processing.
Online resource:	https://doi.org/10.1007/978-981-99-0593-5
ISBN:	9789819905935$q(electronic bk.)

Bayesian real-time system identificationfrom centralized to distributed approach /
Huang, Ke.

Bayesian real-time system identificationfrom centralized to distributed approach /[electronic resource] :by Ke Huang, Ka-Veng Yuen. - Singapore :Springer Nature Singapore :2023. - xii, 276 p. :ill. (chiefly col.), digital ;24 cm.

Chapter 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.

This 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 researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.

ISBN: 9789819905935$q(electronic bk.)

Standard No.: 10.1007/978-981-99-0593-5doiSubjects--Topical Terms:

680472
System identification
--Data processing.

LC Class. No.: QA402

Dewey Class. No.: 003.1

Bayesian real-time system identificationfrom centralized to distributed approach /
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505 0 $a Chapter 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.
520 $a This 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 researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.
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