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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Outlier Detection for Real-Time System Identification,point is defined and derived and this algorithm utilizes it to evaluate the outlierness of each data point. The probability of outlier integrates the normalized residual, the measurement noise level and the size of the dataset, and provides a systematic and objective criterion to effectively screen

兇殘

艱苦地移動(dòng)

Online Distributed Identification for Wireless Sensor Networks, that allows an individual unit to obtain local estimation using part of the data, and the obtained local estimation can then be used as a basis for global estimation. In this chapter, typical architectures of wireless sensor networks will first be introduced, including centralized, decentralized an

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Online Distributed Identification Handling Asynchronous Data and Multiple Outlier-Corrupted Data,nts and multiple outlier-corrupted measurements. These two methods are built based on the online dual-rate distributed identification framework elaborated in Chap. .. First, due to unavoidable imperfection of data acquisition systems, the measurements among different channels are generally asynchron

江湖郎中

Ke Huang,Ka-Veng YuenProvides 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

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刻苦讀書

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https://doi.org/10.1007/978-3-319-18063-2re presented from a Bayesian perspective. In order to formulate the KF algorithm, the state space model of a linear dynamical system is introduced. By using the Bayes’ theorem, the conditional probability density function for prediction can be obtained in a recursive manner and the analytical soluti

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