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001461650 001__ 1461650
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001461650 0247_ $$a10.1007/978-981-99-0593-5$$2doi
001461650 035__ $$aSP(OCoLC)1373985360
001461650 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCF
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001461650 050_4 $$aQA402
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001461650 1001_ $$aHuang, Ke,$$d1989-$$eauthor.
001461650 24510 $$aBayesian real-time system identification :$$bfrom centralized to distributed approach /$$cKe Huang, IKa-Veng Yuen.
001461650 260__ $$aSingapore :$$bSpringer,$$c2023.
001461650 300__ $$a1 online resource (xii, 276 pages) :$$billustrations (chiefly color).
001461650 500__ $$a5.4.3 Procedure of the Real-Time System Identification with Self-Calibratable Model Classes
001461650 504__ $$aIncludes bibliographical references.
001461650 5050_ $$aIntro -- Preface -- Contents -- Nomenclature -- 1 Introduction -- 1.1 Dynamical Systems -- 1.1.1 Time-Invariant Systems -- 1.1.2 Time-Varying Systems -- 1.2 System Identification -- 1.2.1 Problems in System Identification -- 1.2.2 Real-Time System Identification -- 1.2.3 Bayesian System Identification -- 1.3 Uncertainty -- 1.4 Organization of the Book -- References -- 2 System Identification Using Kalman Filter and Extended Kalman Filter -- 2.1 Introduction -- 2.2 Standard Kalman Filter -- 2.2.1 Derivation of the Discrete-Time Kalman Filter -- 2.3 Applications to State Estimation
001461650 5058_ $$a2.3.1 Vehicle Tracking Problem -- 2.3.2 Sixty-Story Building -- 2.4 Extended Kalman Filter -- 2.4.1 Derivation of the Extended Kalman Filter -- 2.4.2 Extended Kalman Filter with Fading Memory -- 2.5 Application to State Estimation and Model Parameter Identification -- 2.5.1 Single-Degree-of-Freedom System -- 2.5.2 Three-Pier Bridge -- 2.5.3 Bouc-Wen Hysteresis System -- 2.6 Application to a Field Inspired Test Case: The Canton Tower -- 2.6.1 Background Information -- 2.6.2 Identification of Structural States and Model Parameters -- 2.7 Extended Readings -- 2.8 Concluding Remarks -- References
001461650 5058_ $$a3 Real-Time Updating of Noise Parameters for System Identification -- 3.1 Introduction -- 3.2 Real-Time Updating of Dynamical Systems and Noise Parameters -- 3.2.1 Updating of States and Model Parameters -- 3.2.2 Updating of Noise Parameters -- 3.3 Efficient Numerical Optimization Scheme -- 3.3.1 Training Phase -- 3.3.2 Working Phase -- 3.3.3 Uncertainty Estimation of the Updated Noise Parameters -- 3.4 Applications -- 3.4.1 Bouc-Wen Hysteresis System -- 3.4.2 Three-Pier Bridge -- 3.5 Concluding Remarks -- References -- 4 Outlier Detection for Real-Time System Identification -- 4.1 Introduction
001461650 5058_ $$a4.2 Outlier Detection Using Probability of Outlier -- 4.2.1 Normalized Residual of Measurement -- 4.2.2 Probability of Outlier -- 4.3 Computational Efficiency Enhancement Techniques -- 4.3.1 Moving Time Window -- 4.3.2 Efficient Screening Criteria -- 4.4 Outlier Detection for Time-Varying Dynamical Systems -- 4.4.1 Training Stage -- 4.4.2 Working Stage -- 4.5 Applications -- 4.5.1 Outlier Generation -- 4.5.2 Single-Degree-of-Freedom Oscillator -- 4.5.3 Fourteen-Bay Truss -- 4.6 Concluding Remarks -- References
001461650 5058_ $$a5 Bayesian Model Class Selection and Self-Calibratable Model Classes for Real-Time System Identification -- 5.1 Introduction -- 5.2 Bayesian Real-Time Model Class Selection -- 5.3 Real-Time System Identification Using Predefined Model Classes -- 5.3.1 Parametric Identification with a Specified Model Class -- 5.3.2 Parametric Identification Using Multiple Model Classes -- 5.3.3 Parametric Identification Using the Most Plausible Model Class -- 5.3.4 Predefined Model Classes -- 5.4 Self-Calibratable Model Classes -- 5.4.1 Parameterization and Model Classes -- 5.4.2 Self-Calibrating Strategy
001461650 506__ $$aAccess limited to authorized users.
001461650 520__ $$aThis book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.
001461650 588__ $$aDescription based on print version record.
001461650 650_0 $$aSystem identification$$xData processing.
001461650 650_0 $$aBayesian statistical decision theory.
001461650 655_0 $$aElectronic books.
001461650 7001_ $$aYuen, Ka-Veng,$$eauthor.
001461650 77608 $$iPrint version:$$aHuang, Ke$$tBayesian Real-Time System Identification$$dSingapore : Springer Singapore Pte. Limited,c2023$$z9789819905928
001461650 852__ $$bebk
001461650 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-0593-5$$zOnline Access$$91397441.1
001461650 909CO $$ooai:library.usi.edu:1461650$$pGLOBAL_SET
001461650 980__ $$aBIB
001461650 980__ $$aEBOOK
001461650 982__ $$aEbook
001461650 983__ $$aOnline
001461650 994__ $$a92$$bISE