| 期刊全称 | Bayesian Real-Time System Identification | | 期刊简称 | From Centralized to | | 影响因子2023 | Ke Huang,Ka-Veng Yuen | | 视频video | | | 发行地址 | 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 | | 图书封面 |  | | 影响因子 | .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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发表于 2025-3-21 20:07:45
