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System Identification : An Introduction - Karel J. Keesman

System Identification

An Introduction

Paperback Published: 29th July 2011
ISBN: 9780857295217
Number Of Pages: 323

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System Identification shows the student reader how to approach the system identification problem in a systematic fashion. The process is divided into three basic steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text.

Following an introduction on system theory, particularly in relation to model representation and model properties, the book contains four parts covering:

- data-based identification - non-parametric methods for use when prior system knowledge is very limited;

- time-invariant identification for systems with constant parameters;

- time-varying systems identification, primarily with recursive estimation techniques; and

- model validation methods.

A fifth part, composed of appendices, covers the various aspects of the underlying mathematics needed to begin using the text.

The book uses essentially semi-physical or gray-box modeling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input-output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various control design methods with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors' Solutions Manual is available from fill in URL here) will both help students to assimilate what they have learned and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques.

Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification will help academic instructors teaching control-related to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.

Industry Reviews

From the reviews:

"The book presents a systematic overview of the fundamental problems and methods in the modern system identification theory. The material is divided into four parts covering data based non-parametric identification methods, time-invariant system identification, time-varying system identification and model validation problems. ... Each chapter of the book is finished with references, historical notes and exercises to be solved by the reader. ... Numerous examples ... demonstrate the practical applicability of the presented methods. The book can be recommended for students and practitioners for self-study." (Zygmunt Hasiewicz, Zentralblatt MATH, Vol. 1230, 2012)

Introductionp. 1
System Theoryp. 1
Terminologyp. 1
Basic Problemsp. 3
Mathematical Modelsp. 5
Model Propertiesp. 6
Structural Model Representationsp. 7
System Identification Procedurep. 10
Historical Notes and Referencesp. 12
Problemsp. 13
Data-based Identification
System Response Methodsp. 17
Impulse Responsep. 17
Impulse Response Model Representationp. 17
Transfer Function Model Representationp. 18
Direct Impulse Response Identificationp. 20
Step Responsep. 22
Direct Step Response Identificationp. 22
Impulse Response Identification Using Step Responsesp. 23
Sine-wave Responsep. 24
Frequency Transfer Functionp. 24
Sine-wave Response Identificationp. 24
Historical Notes and Referencesp. 26
Problemsp. 26
Frequency Response Methodsp. 29
Empirical Transfer-function Identificationp. 29
Sine Wave Testingp. 29
Discrete Fourier Transform of Signalsp. 30
Empirical Transfer-function Estimatep. 31
Critical Point Identificationp. 34
Discrete-time Transfer Functionp. 36
z-Transformp. 36
Impulse Response Identification Using Input-output Datap. 37
Discrete-time Delta Operatorp. 39
Historical Notes and Referencesp. 40
Problemsp. 40
Correlation Methodsp. 43
Correlation Functionsp. 43
Autocorrelation Functionp. 43
White Noise Sequencep. 45
Cross-correlation Functionp. 45
Wiener-Hopf Relationshipp. 47
Wiener-Hopf Equationp. 47
Impulse Response Identification Using Wiener-Hopf Equationp. 47
Random Binary Sequencesp. 49
Filter Properties of Wiener-Hopf Relationshipp. 50
Frequency Analysis Using Correlation Techniquesp. 51
Cross-correlation Between Input-output Sine Wavesp. 51
Transfer-function Estimate Using Correlation Techniquesp. 52
Spectral Analysisp. 52
Power Spectrap. 52
Transfer-function Estimate Using Power Spectrap. 54
Bias-variance Tradeoff in Transfer-function Estimatesp. 55
Historical Notes and Referencesp. 57
Problemsp. 57
Time-invariant Systems Identification
Static Systems Identificationp. 61
Linear Static Systemsp. 61
Linear Regressionp. 61
Least-squares Estimationp. 62
Interpretation of Least-squares Methodp. 66
Biasp. 69
Accuracyp. 72
Identifiabilityp. 77
*Errors-in-variables Problemp. 85
*Bounded-noise Problem: Linear Casep. 88
Nonlinear Static Systemsp. 92
Nonlinear Regressionp. 92
Nonlinear Least-squares Estimationp. 93
Iterative Solutionsp. 94
Accuracyp. 97
Model Reparameterization: Static Casep. 99
*Maximum Likelihood Estimationp. 101
*Bounded-noise Problem: Nonlinear Casep. 105
Historical Notes and Referencesp. 109
Problemsp. 110
Dynamic Systems Identificationp. 113
Linear Dynamic Systemsp. 113
Transfer Function Modelsp. 113
Equation Error Identificationp. 117
Output Error Identificationp. 121
Prediction Error Identificationp. 127
Model Structure Identificationp. 132
*Subspace Identificationp. 135
*Linear Parameter-varying Model Identificationp. 140
*Orthogonal Basis Functionsp. 147
*Closed-loop Identificationp. 148
Nonlinear Dynamic Systemsp. 152
Simulation Modelsp. 152
*Parameter Sensitivityp. 153
Nonlinear Regressionsp. 156
Iterative Solutionp. 156
Model Reparameterization: Dynamic Casep. 157
Historical Notes and Referencesp. 163
Problemsp. 165
Time-varying Systems Identification
Time-varying Static Systems Identificationp. 169
Linear Regression Modelsp. 169
Recursive Estimationp. 169
Time-varying Parametersp. 174
Multioutput Casep. 177
Resemblance with Kalman Filterp. 182
*Numerical Issuesp. 184
Nonlinear Static Systemsp. 187
State-space Representationp. 187
Extended Kalman Filterp. 189
Historical Notes and Referencesp. 191
Problemsp. 192
Time-varying Dynamic Systems Identificationp. 195
Linear Dynamic Systemsp. 195
Recursive Least-squares Estimationp. 195
Recursive Prediction Error Estimationp. 199
Smoothingp. 206
Nonlinear Dynamic Systemsp. 209
Extended Kalman Filteringp. 209
*Observer-based Methodsp. 213
Historical Notes and Referencesp. 215
Problemp. 217
Model Validation
Model Validation Techniquesp. 225
Prior Knowledgep. 225
Experience with Modelp. 226
Model Reductionp. 226
Simulationp. 227
Predictionp. 230
Experimental Datap. 231
Graphical Inspectionp. 231
Correlation Testsp. 233
Historical Notes and Referencesp. 245
Outlookp. 246
Problemsp. 246
Matrix Algebrap. 249
Basic Definitionsp. 249
Important Operationsp. 250
Quadratic Matrix Formsp. 252
Vector and Matrix Normsp. 253
Differentiation of Vectors and Matricesp. 254
Eigenvalues and Eigenvectorsp. 256
Range and Kernel of a Matrixp. 258
Exponential of a Matrixp. 259
Square Root of a Matrixp. 260
Choleski Decompositionp. 261
Modified Choleski (UD) Decompositionp. 262
QR Decompositionp. 262
Singular Value Decompositionp. 263
Projection Matricesp. 264
Statisticsp. 267
Random Entitiesp. 267
Discrete/Continuous Random Variablesp. 267
Random Vectorsp. 268
Stochastic Processesp. 272
Laplace, Fourier, and z-Transformsp. 275
Laplace Transformp. 275
Fourier Transformp. 277
z-Transformp. 277
Bode Diagramsp. 281
The Bode Plotp. 281
Four Basic Typesp. 282
Constant or K Factorp. 282
(jw)$$$n Factorp. 282
(1 + jwT)$$$m Factorp. 282
ejwt Factorp. 284
Shift Operator Calculusp. 287
Forward- and Backward-shift Operatorp. 287
Pulse Transfer Operatorp. 289
Recursive Least-squares Derivationp. 293
Least-squares Methodp. 293
Equivalent Recursive Formp. 294
Dissolved Oxygen Datap. 297
Referencesp. 303
Indexp. 317
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780857295217
ISBN-10: 0857295217
Series: Advanced Textbooks in Control and Signal Processing
Audience: General
Format: Paperback
Language: English
Number Of Pages: 323
Published: 29th July 2011
Country of Publication: GB
Dimensions (cm): 23.11 x 15.49  x 2.29
Weight (kg): 0.5

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