Bayesian Real-Time System Identification
From Centralized to Distributed Approach
Ka-Veng Yuen author Ke Huang author
Format:Hardback
Publisher:Springer Verlag, Singapore
Published:21st Mar '23
Should be back in stock very soon
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 researchersin civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.
“The presented centralized and distributed framework for Bayesian real-time identification holds great potential for applications beyond civil engineering, including mechanical systems and aerospace structures, the more so that Bayesian multi-sensor data fusion is becoming widespread in engineering practice.” (Dariusz Uciński, zbMATH 1536.93001, 2024)
ISBN: 9789819905928
Dimensions: unknown
Weight: unknown
276 pages
2023 ed.