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Table of Contents
Intro
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
2.3.1 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
3 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
4.2 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
5 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
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
2.3.1 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
3 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
4.2 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
5 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