Dynamic system identification : experiment design and data analysis / Graham C. Goodwin and Robert L. Payne.

Format:

Book

Author/Creator:

Goodwin, Graham C. (Graham Clifford), 1945-

Contributor:

Payne, Robert L.

Series:

Mathematics in science and engineering ; v 136.

Mathematics in science and engineering ; v 136

Language:

English

Subjects (All):

System analysis.

Mathematical models.

Experimental design.

Physical Description:

1 online resource (303 p.)

Place of Publication:

New York : Academic Press, 1977.

Language Note:

English

Summary:

Dynamic system identification : experiment design and data analysis

Contents:

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 Covariance2.5 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 ConclusionsProblems; 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 Algorithms7.1 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 TheoremsB.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

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-282-28926-8

9786612289262

0-08-095645-9

OCLC:

316568253