| Description |
1 online resource (x, 291 pages) : illustrations |
| Series |
Mathematics in science and engineering ; v. 136 |
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Mathematics in science and engineering ; v. 136.
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| 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 |
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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) |
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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 |
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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 |
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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 |
| Summary |
Dynamic system identification : experiment design and data analysis |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL |
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Print version record |
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digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL |
| Subject |
System analysis
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Mathematical models.
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Experimental design.
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Systems Analysis
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Models, Theoretical
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Research Design
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systems analysis.
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mathematical models.
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MATHEMATICS -- General.
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Experimental design
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Mathematical models
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System analysis
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Modellierung
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Systemanalyse
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Versuchsanlage
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| Form |
Electronic book
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| Author |
Payne, Robert L
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| ISBN |
9780122897504 |
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0122897501 |
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9780080956459 |
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0080956459 |
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