Bayesian Real-Time System Identification
From Centralized to Distributed Approach
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Beschreibung
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.
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.
Provides two different perspectives to data processing for system identification Addresses the challenging problems in real-time system identification Provides an easy way to help the readers better master the algorithms and understand the applications
Autor*in
Ke Huang
Themen in »Bayesian Real-Time System Identification«
Real-time System Identification Centralized Identification Distributed Identification Parameter Estimation Model class Selection Bayesian Inference Structural Health Monitoring
Stimmen zu »Bayesian Real-Time System Identification«
“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)
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