Bayesian Real-time System Identification: From Centralized to Distributed Approach - Rilegato

Huang, Ke; Yuen, Ka-veng

Rilegato

ISBN 10: 9819905923 ISBN 13: 9789819905928

Casa editrice: Springer Nature, 2023

Riassunto

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.

Su questo libro

Informazioni sull?autore

?Ke Huang received her Ph.D. in civil engineering from the University of Macau. She is currently Assistant Professor of the School of Civil Engineering at the Changsha University of Science and Technology. Her research expertise includes substructural identification, distributed identification, and online estimation.

Ka-Veng Yuen received his Ph.D. in civil engineering from the California Institute of Technology. He is Distinguished Professor of Civil and Environmental Engineering at the University of Macau. The research expertise of Prof. KV Yuen includes Bayesian inference, uncertainty quantification, system identification, structural health monitoring, reliability analysis, and analysis of dynamical systems. He is Single Author of the book “Bayesian Methods for Structural Dynamics and Civil Engineering” published by John Wiley and Sons. He is also Recipient of the Young Investigator Award of the International Chinese Association on Computational Mechanics in 2011. He is Editorial Board Member of Computer-Aided Civil and Infrastructure Engineering, Structural Control and Health Monitoring, and International Journal for Uncertainty Quantification, etc.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9819905923
• ISBN 13: 9789819905928
• Rilegatura: Copertina rigida
• Numero di pagine: 288
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania