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Titlebook: Bayesian Real-Time System Identification; From Centralized to Ke Huang,Ka-Veng Yuen Book 2023 The Editor(s) (if applicable) and The Author

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樓主
發(fā)表于 2025-3-21 20:07:45 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Bayesian Real-Time System Identification
期刊簡稱From Centralized to
影響因子2023Ke Huang,Ka-Veng Yuen
視頻videohttp://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Titlebook: Bayesian Real-Time System Identification; From Centralized to  Ke Huang,Ka-Veng Yuen Book 2023 The Editor(s) (if applicable) and The Author
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..
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發(fā)表于 2025-3-21 23:18:24 | 只看該作者
tification.Provides an easy way to help the readers better m.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 iden
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發(fā)表于 2025-3-22 02:08:07 | 只看該作者
Book 2023gorithms 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..
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https://doi.org/10.1007/978-3-319-18063-2d the procedures of the EKF algorithm are formulated in the same manner as the standard KF algorithm. The EKF with fading memory is introduced to enhance the tracking capability for time-varying systems. Applications to simultaneous states and model parameters estimation are presented. The KF algori
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Human Green Development Report 2014t in the extended Kalman filter, the proposed method enhances the applicability of the real-time system identification algorithm for nonstationary circumstances generally encountered in practice. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are
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發(fā)表于 2025-3-22 17:37:56 | 只看該作者
Human Green Development Report 2014emove the outliers in the measurements and identify the time-varying systems simultaneously. By excluding the outliers in the measurements, the proposed algorithm ensures the stability and reliability of the estimation. Examples are presented to illustrate the practical aspects of detecting outliers
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Theoretical Rationale Behind HGDItification results are not affected by asynchronism of different sensor nodes. The proposed approach utilizes directly asynchronous data for online system identification. Regarding the second issue of outlier contamination, a hierarchical outlier detection approach is introduced. It detects the loca
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