Dynamic system identification :: experiment design and data analysis /
Dynamic system identification : experiment design and data analysis.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Academic Press,
1977.
|
| Schriftenreihe: | Mathematics in science and engineering ;
v. 136. |
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | Dynamic system identification : experiment design and data analysis. |
| Beschreibung: | 1 online resource (x, 291 pages) : illustrations |
| Format: | 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. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780122897504 0122897501 9780080956459 0080956459 |
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| 100 | 1 | |a Goodwin, Graham C. |q (Graham Clifford), |d 1945- |1 https://id.oclc.org/worldcat/entity/E39PBJvHd8R3xv9C3gHbyb4pfq |0 http://id.loc.gov/authorities/names/n83133563 | |
| 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. | ||
| 300 | |a 1 online resource (x, 291 pages) : |b illustrations | ||
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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 | |
| 650 | 0 | |a System analysis. | |
| 650 | 0 | |a Mathematical models. |0 http://id.loc.gov/authorities/subjects/sh85082124 | |
| 650 | 0 | |a Experimental design. |0 http://id.loc.gov/authorities/subjects/sh85046441 | |
| 650 | 2 | |a Systems Analysis |0 https://id.nlm.nih.gov/mesh/D013597 | |
| 650 | 2 | |a Models, Theoretical |0 https://id.nlm.nih.gov/mesh/D008962 | |
| 650 | 2 | |a Research Design |0 https://id.nlm.nih.gov/mesh/D012107 | |
| 650 | 6 | |a Analyse de systèmes. | |
| 650 | 6 | |a Modèles mathématiques. | |
| 650 | 6 | |a Plan d'expérience. | |
| 650 | 7 | |a systems analysis. |2 aat | |
| 650 | 7 | |a mathematical models. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x General. |2 bisacsh | |
| 650 | 7 | |a Experimental design |2 fast | |
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| 700 | 1 | |a Payne, Robert L. | |
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| author | Goodwin, Graham C. (Graham Clifford), 1945- |
| author2 | Payne, Robert L. |
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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 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 |
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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)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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. 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| id | ZDB-4-EBA-ocn316568253 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:42Z |
| institution | BVB |
| isbn | 9780122897504 0122897501 9780080956459 0080956459 |
| language | English |
| oclc_num | 316568253 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (x, 291 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 1977 |
| publishDateSearch | 1977 |
| publishDateSort | 1977 |
| publisher | Academic Press, |
| record_format | marc |
| series | Mathematics in science and engineering ; |
| series2 | Mathematics in science and engineering ; |
| spelling | Goodwin, Graham C. (Graham Clifford), 1945- https://id.oclc.org/worldcat/entity/E39PBJvHd8R3xv9C3gHbyb4pfq http://id.loc.gov/authorities/names/n83133563 Dynamic system identification : experiment design and data analysis / Graham C. Goodwin and Robert L. Payne. New York : Academic Press, 1977. 1 online resource (x, 291 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Mathematics in science and engineering ; v. 136 Includes bibliographical references and index. Dynamic system identification : experiment design and data analysis. Print version record. Use copy Restrictions unspecified star MiAaHDL Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010. MiAaHDL 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 digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL 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 System analysis. Mathematical models. http://id.loc.gov/authorities/subjects/sh85082124 Experimental design. http://id.loc.gov/authorities/subjects/sh85046441 Systems Analysis https://id.nlm.nih.gov/mesh/D013597 Models, Theoretical https://id.nlm.nih.gov/mesh/D008962 Research Design https://id.nlm.nih.gov/mesh/D012107 Analyse de systèmes. Modèles mathématiques. Plan d'expérience. systems analysis. aat mathematical models. aat MATHEMATICS General. bisacsh Experimental design fast Mathematical models fast System analysis fast Modellierung gnd http://d-nb.info/gnd/4170297-9 Systemanalyse gnd http://d-nb.info/gnd/4116673-5 Versuchsanlage gnd http://d-nb.info/gnd/4280406-1 Payne, Robert L. has work: Dynamic system identification (Text) https://id.oclc.org/worldcat/entity/E39PCG8XJrTYvHxVpBPC3c9cmq https://id.oclc.org/worldcat/ontology/hasWork Print version: Goodwin, Graham C. (Graham Clifford), 1945- Dynamic system identification. New York : Academic Press, 1977 9780122897504 (DLC) 76050396 (OCoLC)3088727 Mathematics in science and engineering ; v. 136. http://id.loc.gov/authorities/names/n42015986 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297038 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/bookseries/00765392/136 Volltext |
| spellingShingle | Goodwin, Graham C. (Graham Clifford), 1945- Dynamic system identification : experiment design and data analysis / Mathematics in science and engineering ; 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 System analysis. Mathematical models. http://id.loc.gov/authorities/subjects/sh85082124 Experimental design. http://id.loc.gov/authorities/subjects/sh85046441 Systems Analysis https://id.nlm.nih.gov/mesh/D013597 Models, Theoretical https://id.nlm.nih.gov/mesh/D008962 Research Design https://id.nlm.nih.gov/mesh/D012107 Analyse de systèmes. Modèles mathématiques. Plan d'expérience. systems analysis. aat mathematical models. aat MATHEMATICS General. bisacsh Experimental design fast Mathematical models fast System analysis fast Modellierung gnd http://d-nb.info/gnd/4170297-9 Systemanalyse gnd http://d-nb.info/gnd/4116673-5 Versuchsanlage gnd http://d-nb.info/gnd/4280406-1 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85082124 http://id.loc.gov/authorities/subjects/sh85046441 https://id.nlm.nih.gov/mesh/D013597 https://id.nlm.nih.gov/mesh/D008962 https://id.nlm.nih.gov/mesh/D012107 http://d-nb.info/gnd/4170297-9 http://d-nb.info/gnd/4116673-5 http://d-nb.info/gnd/4280406-1 |
| title | Dynamic system identification : experiment design and data analysis / |
| title_auth | Dynamic system identification : experiment design and data analysis / |
| title_exact_search | Dynamic system identification : experiment design and data analysis / |
| title_full | Dynamic system identification : experiment design and data analysis / Graham C. Goodwin and Robert L. Payne. |
| title_fullStr | Dynamic system identification : experiment design and data analysis / Graham C. Goodwin and Robert L. Payne. |
| title_full_unstemmed | Dynamic system identification : experiment design and data analysis / Graham C. Goodwin and Robert L. Payne. |
| title_short | Dynamic system identification : |
| title_sort | dynamic system identification experiment design and data analysis |
| title_sub | experiment design and data analysis / |
| topic | System analysis. Mathematical models. http://id.loc.gov/authorities/subjects/sh85082124 Experimental design. http://id.loc.gov/authorities/subjects/sh85046441 Systems Analysis https://id.nlm.nih.gov/mesh/D013597 Models, Theoretical https://id.nlm.nih.gov/mesh/D008962 Research Design https://id.nlm.nih.gov/mesh/D012107 Analyse de systèmes. Modèles mathématiques. Plan d'expérience. systems analysis. aat mathematical models. aat MATHEMATICS General. bisacsh Experimental design fast Mathematical models fast System analysis fast Modellierung gnd http://d-nb.info/gnd/4170297-9 Systemanalyse gnd http://d-nb.info/gnd/4116673-5 Versuchsanlage gnd http://d-nb.info/gnd/4280406-1 |
| topic_facet | System analysis. Mathematical models. Experimental design. Systems Analysis Models, Theoretical Research Design Analyse de systèmes. Modèles mathématiques. Plan d'expérience. systems analysis. mathematical models. MATHEMATICS General. Experimental design Mathematical models System analysis Modellierung Systemanalyse Versuchsanlage |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297038 https://www.sciencedirect.com/science/bookseries/00765392/136 |
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