Produktbild: Identification of Physical Systems

Identification of Physical Systems

148,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.05.2014

Verlag

John Wiley & Sons

Seitenzahl

544

Maße (L/B/H)

25,1/17,2/3,2 cm

Gewicht

947 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-99012-3

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

12.05.2014

Verlag

John Wiley & Sons

Seitenzahl

544

Maße (L/B/H)

25,1/17,2/3,2 cm

Gewicht

947 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-99012-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Identification of Physical Systems
  • Preface xv
     
    Nomenclature xxi
     
    1 Modeling of Signals and Systems 1
     
    1.1 Introduction 1
     
    1.2 Classification of Signals 2
     
    1.3 Model of Systems and Signals 5
     
    1.4 Equivalence of Input-Output and State-Space Models 8
     
    1.5 Deterministic Signals 11
     
    1.6 Introduction to Random Signals 23
     
    1.7 Model of Random Signals 28
     
    1.8 Model of a System with Disturbance and Measurement Noise 41
     
    1.9 Summary 50
     
    References 54
     
    Further Readings 54
     
    2 Characterization of Signals: Correlation and Spectral Density 57
     
    2.1 Introduction 57
     
    2.2 Definitions of Auto- and Cross-Correlation (and Covariance) 58
     
    2.3 Spectral Density: Correlation in the Frequency Domain 67
     
    2.4 Coherence Spectrum 74
     
    2.5 Illustrative Examples in Correlation and Spectral Density 76
     
    2.6 Input-Output Correlation and Spectral Density 91
     
    2.7 Illustrative Examples: Modeling and Identification 98
     
    2.8 Summary 109
     
    2.9 Appendix 112
     
    References 116
     
    3 Estimation Theory 117
     
    3.1 Overview 117
     
    3.2 Map Relating Measurement and the Parameter 119
     
    3.3 Properties of Estimators 123
     
    3.4 Cramér-Rao Inequality 127
     
    3.5 Maximum Likelihood Estimation 139
     
    3.6 Summary 154
     
    3.7 Appendix: Cauchy-Schwarz Inequality 157
     
    3.8 Appendix: Cram´er-Rao Lower Bound 157
     
    3.9 Appendix: Fisher Information: Cauchy PDF 161
     
    3.10 Appendix: Fisher Information for i.i.d. PDF 161
     
    3.11 Appendix: Projection Operator 162
     
    3.12 Appendix: Fisher Information: Part Gauss-Part Laplace 164
     
    Problem 165
     
    References 165
     
    Further Readings 165
     
    4 Estimation of Random Parameter 167
     
    4.1 Overview 167
     
    4.2 Minimum Mean-Squares Estimator (MMSE): Scalar Case 167
     
    4.3 MMSE Estimator: Vector Case 169
     
    4.4 Expression for Conditional Mean 172
     
    4.5 Summary 183
     
    4.6 Appendix: Non-Gaussian Measurement PDF 184
     
    References 188
     
    Further Readings 188
     
    5 Linear Least-Squares Estimation 189
     
    5.1 Overview 189
     
    5.2 Linear Least-Squares Approach 189
     
    5.3 Performance of the Least-Squares Estimator 195
     
    5.4 Illustrative Examples 205
     
    5.5 Cram´er-Rao Lower Bound 209
     
    5.6 Maximum Likelihood Estimation 210
     
    5.7 Least-Squares Solution of Under-Determined System 212
     
    5.8 Singular Value Decomposition 213
     
    5.9 Summary 218
     
    5.10 Appendix: Properties of the Pseudo-Inverse and the Projection Operator 221
     
    5.11 Appendix: Positive Definite Matrices 222
     
    5.12 Appendix: Singular Value Decomposition of a Matrix 223
     
    5.13 Appendix: Least-Squares Solution for Under-Determined System 228
     
    5.14 Appendix: Computation of Least-Squares Estimate Using the SVD 229
     
    References 229
     
    Further Readings 230
     
    6 Kalman Filter 231
     
    6.1 Overview 231
     
    6.2 Mathematical Model of the System 233
     
    6.3 Internal Model Principle 236
     
    6.4 Duality Between Controller and an Estimator Design 244
     
    6.5 Observer: Estimator for the States of a System 246
     
    6.6 Kalman Filter: Estimator of the States of a Stochastic System 250
     
    6.7 The Residual of the Kalman Filter with Model Mismatch and Non-Optimal Gain 267
     
    6.8 Summary 274
     
    6.9 Appendix: Estimation Error Covariance and the Kalman Gain 277
     
    6.10 Appendix: The Role of the Ratio of Plant and the Measurement Noise Variances 279
     
    6.11