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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Introduction,ng Bayesian methods are briefly introduced. Finally, an overview of this book is given with outline of each chapter for the convenience of readers. This book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for

Feature

System Identification Using Kalman Filter and Extended Kalman Filter,d 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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Bayesian Model Class Selection and Self-Calibratable Model Classes for Real-Time System Identificatew third level of system identification is presented to resolve this problem by using self-calibratable model classes. This self-calibrating strategy can correct the deficiencies of the model classes and achieve reliable real-time identification results for time-varying dynamical systems. On the oth

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Online Distributed Identification for Wireless Sensor Networks,smitted from the sensor nodes in order to obtain reliable global estimation. As a result, the large identification uncertainty in the local identification results can be substantially reduced. In addition to data compression, a dual-rate strategy for sampling and transmission/fusion is used to allev

缺陷

Online Distributed Identification Handling Asynchronous Data and Multiple Outlier-Corrupted Data,tification 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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Introduction,uced. System identification is the problem of building mathematical models describing the behavior of a dynamical system based on the observations from the system. Traditionally, there are two levels of system identification problems, i.e., estimation of the uncertain parameters governed in the math

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