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Dynamic system identification : experiment design and data analysis /

Dynamic system identification : experiment design and data analysis.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Goodwin, Graham C. (Graham Clifford), 1945-
Otros Autores: Payne, Robert L.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Academic Press, 1977.
Colección:Mathematics in science and engineering ; v. 136.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Goodwin, Graham C.  |q (Graham Clifford),  |d 1945- 
245 1 0 |a Dynamic system identification :  |b experiment design and data analysis /  |c Graham C. Goodwin and Robert L. Payne. 
260 |a New York :  |b Academic Press,  |c 1977. 
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490 1 |a Mathematics in science and engineering ;  |v v. 136 
504 |a Includes bibliographical references and index. 
520 |a Dynamic system identification : experiment design and data analysis. 
588 0 |a Print version record. 
506 |3 Use copy  |f Restrictions unspecified  |2 star  |5 MiAaHDL 
533 |a Electronic reproduction.  |b [Place of publication not identified] :  |c HathiTrust Digital Library,  |d 2010.  |5 MiAaHDL 
538 |a 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.  |u http://purl.oclc.org/DLF/benchrepro0212  |5 MiAaHDL 
583 1 |a digitized  |c 2010  |h HathiTrust Digital Library  |l committed to preserve  |2 pda  |5 MiAaHDL 
505 0 |a 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 
505 8 |a 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) 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
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650 0 |a Experimental design. 
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650 2 |a Models, Theoretical 
650 2 |a Research Design 
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650 6 |a Modèles mathématiques. 
650 6 |a Plan d'expérience. 
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650 7 |a Experimental design  |2 fast 
650 7 |a Mathematical models  |2 fast 
650 7 |a System analysis  |2 fast 
650 7 |a Modellierung  |2 gnd 
650 7 |a Systemanalyse  |2 gnd 
650 7 |a Versuchsanlage  |2 gnd 
700 1 |a Payne, Robert L. 
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