Dynamic System Identification: Experiment Design and Data Analysis
Published on 10. November 1977
290 pages
978-0-08-095645-9 (ISBN)
Description
Dynamic System Identification: Experiment Design and Data Analysis
More details
Other editions
Additional editions
Graham C. Goodwin | Robert L. Payne
Dynamic System Identification
Experiment Design and Data Analysis
Book
01/1977
Academic Press
€69.95
Article exhausted; check different version
Content
- Front Cover
- Dynamic System Identification: Experiment Design and Data Analysis
- Copyright Page
- Contents
- Preface
- Chapter 1. Introduction and Statistical Background
- 1.1 Introduction
- 1.2 Probability Theory
- 1.3 Point Estimation Theory
- 1.4 Sufficient Statistics
- 1.5 Hypothesis Testing
- 1.6 The Bayesian Decision Theory Approach
- 1.7 Information Theory Approach
- 1.8 Commonly Used Estimators
- 1.9 Conclusions
- Problems
- Chapter 2. Linear Least Squares and Normal Theory
- 2.1 Introduction
- 2.2 The Least Squares Solution
- 2.3 Best Linear Unbiased Estimators
- 2.4 Unbiased Estimation of BLUE Covariance
- 2.5 Normal Theory
- 2.6 Numerical Aspects
- 2.7 Conclusions
- Problems
- Chapter 3. Maximum Likelihood Estimators
- 3.1 Introduction
- 3.2 The Likelihood Function and the ML Estimator
- 3.3 Maximum Likelihood for the Normal Linear Model
- 3.4 General Properties
- 3.5 Asymptotic Properties
- 3.6 The Likelihood Ratio Test
- 3.7 Conclusions
- Problems
- Chapter 4. Models for Dynamic Systems
- 4.1 Introduction
- 4.2 Deterministic Models
- 4.3 Canonical Models
- 4.4 Stochastic Models (The Covariance Stationary Case)
- 4.5 Stochastic Models (Prediction Error Formulation)
- 4.6 Conclusions
- Problems
- Chapter 5. Estimation for Dynamic Systems
- 5.1 Introduction
- 5.2 Least Squares for Linear Dynamic Systems
- 5.3 Consistent Estimators for Linear Dynamic Systems
- 5.4 Prediction Error Formulation and Maximum Likelihood
- 5.5 Asymptotic Properties
- 5.6 Estimation in Closed Loop
- 5.7 Conclusions
- Problems
- Chapter 6. Experiment Design
- 6.1 Introduction
- 6.2 Design Criteria
- 6.3 Time Domain Design of Input Signals
- 6.4 Frequency Domain Design of Input Signals
- 6.5 Sampling Strategy Design
- 6.6 Design for Structure Discrimination
- 6.7 Conclusions
- Problems
- Chapter 7. Recursive Algorithms
- 7.1 Introduction
- 7.2 Recursive Least Squares
- 7.3 Time Varying Parameters
- 7.4 Further Recursive Estimators for Dynamic Systems
- 7.5 Stochastic Approximation
- 7.6 Convergence of Recursive Estimators
- 7.7 Recursive Experiment Design
- 7.8 Stochastic Control
- 7.9 Conclusions
- Problems
- Appendix A. Summary of Results from Distribution Theory
- A.1 Characteristic Function
- A.2 The Normal Distribution
- A.3 The ?2 ("Chi Squared") Distribution
- A.4 The "F" Distribution
- A.5 The Student t Distribution
- A.6 The Fisher-Cochrane Theorem
- A.7 The Noncentral ?2 Distribution
- Appendix B. Limit Theorems
- B.1 Convergence of Random Variables
- B.2 Relationships between Convergence Concepts
- B.3 Some Important Convergence Theorems
- Appendix C. Stochastic Processes
- C.1 Basic Results
- C.2 Continuous Time Stochastic Processes
- C.3 Spectral Representation of Stochastic Processes
- Appendix D. Martingale Convergence Results
- D.1 Toeplitz and Kronecker Lemmas
- D.2 Martingales
- Appendix E. Mathematical Results
- E.l Matrix Results
- E.2 Vector and Matrix Differentiation Results
- E.3 Caratheodory's Theorem
- Problem Solutions
- References
- Index
