Bayesian Real-Time System Identification

​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.

“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)

• 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.