期刊全稱 | Bayesian Real-Time System Identification | 期刊簡稱 | From Centralized to | 影響因子2023 | Ke Huang,Ka-Veng Yuen | 視頻video | http://file.papertrans.cn/182/181877/181877.mp4 | 發(fā)行地址 | 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 m | 圖書封面 |  | 影響因子 | .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.. | Pindex | Book 2023 |
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