System Identification

Name: System Identification
Rating: 0 (0 reviews)
ISBN: 9780857295217

An Introduction

By: Karel J. Keesman

Be the first to write a review

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

Paperback

$125.05

or 4 easy payments of $31.26 with

Learn more

BUY NOW

Add to Wishlist

Ships in 10 to 15 business days

Earn 250 Qantas Points
on this Book

Description
Table of Contents
Product Details

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)

Introduction	p. 1
System Theory	p. 1
Terminology	p. 1
Basic Problems	p. 3
Mathematical Models	p. 5
Model Properties	p. 6
Structural Model Representations	p. 7
System Identification Procedure	p. 10
Historical Notes and References	p. 12
Problems	p. 13
Data-based Identification
System Response Methods	p. 17
Impulse Response	p. 17
Impulse Response Model Representation	p. 17
Transfer Function Model Representation	p. 18
Direct Impulse Response Identification	p. 20
Step Response	p. 22
Direct Step Response Identification	p. 22
Impulse Response Identification Using Step Responses	p. 23
Sine-wave Response	p. 24
Frequency Transfer Function	p. 24
Sine-wave Response Identification	p. 24
Historical Notes and References	p. 26
Problems	p. 26
Frequency Response Methods	p. 29
Empirical Transfer-function Identification	p. 29
Sine Wave Testing	p. 29
Discrete Fourier Transform of Signals	p. 30
Empirical Transfer-function Estimate	p. 31
Critical Point Identification	p. 34
Discrete-time Transfer Function	p. 36
z-Transform	p. 36
Impulse Response Identification Using Input-output Data	p. 37
Discrete-time Delta Operator	p. 39
Historical Notes and References	p. 40
Problems	p. 40
Correlation Methods	p. 43
Correlation Functions	p. 43
Autocorrelation Function	p. 43
White Noise Sequence	p. 45
Cross-correlation Function	p. 45
Wiener-Hopf Relationship	p. 47
Wiener-Hopf Equation	p. 47
Impulse Response Identification Using Wiener-Hopf Equation	p. 47
Random Binary Sequences	p. 49
Filter Properties of Wiener-Hopf Relationship	p. 50
Frequency Analysis Using Correlation Techniques	p. 51
Cross-correlation Between Input-output Sine Waves	p. 51
Transfer-function Estimate Using Correlation Techniques	p. 52
Spectral Analysis	p. 52
Power Spectra	p. 52
Transfer-function Estimate Using Power Spectra	p. 54
Bias-variance Tradeoff in Transfer-function Estimates	p. 55
Historical Notes and References	p. 57
Problems	p. 57
Time-invariant Systems Identification
Static Systems Identification	p. 61
Linear Static Systems	p. 61
Linear Regression	p. 61
Least-squares Estimation	p. 62
Interpretation of Least-squares Method	p. 66
Bias	p. 69
Accuracy	p. 72
Identifiability	p. 77
^*Errors-in-variables Problem	p. 85
^*Bounded-noise Problem: Linear Case	p. 88
Nonlinear Static Systems	p. 92
Nonlinear Regression	p. 92
Nonlinear Least-squares Estimation	p. 93
Iterative Solutions	p. 94
Accuracy	p. 97
Model Reparameterization: Static Case	p. 99
^*Maximum Likelihood Estimation	p. 101
^*Bounded-noise Problem: Nonlinear Case	p. 105
Historical Notes and References	p. 109
Problems	p. 110
Dynamic Systems Identification	p. 113
Linear Dynamic Systems	p. 113
Transfer Function Models	p. 113
Equation Error Identification	p. 117
Output Error Identification	p. 121
Prediction Error Identification	p. 127
Model Structure Identification	p. 132
^*Subspace Identification	p. 135
^*Linear Parameter-varying Model Identification	p. 140
^*Orthogonal Basis Functions	p. 147
^*Closed-loop Identification	p. 148
Nonlinear Dynamic Systems	p. 152
Simulation Models	p. 152
^*Parameter Sensitivity	p. 153
Nonlinear Regressions	p. 156
Iterative Solution	p. 156
Model Reparameterization: Dynamic Case	p. 157
Historical Notes and References	p. 163
Problems	p. 165
Time-varying Systems Identification
Time-varying Static Systems Identification	p. 169
Linear Regression Models	p. 169
Recursive Estimation	p. 169
Time-varying Parameters	p. 174
Multioutput Case	p. 177
Resemblance with Kalman Filter	p. 182
^*Numerical Issues	p. 184
Nonlinear Static Systems	p. 187
State-space Representation	p. 187
Extended Kalman Filter	p. 189
Historical Notes and References	p. 191
Problems	p. 192
Time-varying Dynamic Systems Identification	p. 195
Linear Dynamic Systems	p. 195
Recursive Least-squares Estimation	p. 195
Recursive Prediction Error Estimation	p. 199
Smoothing	p. 206
Nonlinear Dynamic Systems	p. 209
Extended Kalman Filtering	p. 209
^*Observer-based Methods	p. 213
Historical Notes and References	p. 215
Problem	p. 217
Model Validation
Model Validation Techniques	p. 225
Prior Knowledge	p. 225
Experience with Model	p. 226
Model Reduction	p. 226
Simulation	p. 227
Prediction	p. 230
Experimental Data	p. 231
Graphical Inspection	p. 231
Correlation Tests	p. 233
Historical Notes and References	p. 245
Outlook	p. 246
Problems	p. 246
Matrix Algebra	p. 249
Basic Definitions	p. 249
Important Operations	p. 250
Quadratic Matrix Forms	p. 252
Vector and Matrix Norms	p. 253
Differentiation of Vectors and Matrices	p. 254
Eigenvalues and Eigenvectors	p. 256
Range and Kernel of a Matrix	p. 258
Exponential of a Matrix	p. 259
Square Root of a Matrix	p. 260
Choleski Decomposition	p. 261
Modified Choleski (UD) Decomposition	p. 262
QR Decomposition	p. 262
Singular Value Decomposition	p. 263
Projection Matrices	p. 264
Statistics	p. 267
Random Entities	p. 267
Discrete/Continuous Random Variables	p. 267
Random Vectors	p. 268
Stochastic Processes	p. 272
Laplace, Fourier, and z-Transforms	p. 275
Laplace Transform	p. 275
Fourier Transform	p. 277
z-Transform	p. 277
Bode Diagrams	p. 281
The Bode Plot	p. 281
Four Basic Types	p. 282
Constant or K Factor	p. 282
(jw)^$$$n Factor	p. 282
(1 + jwT)^$$$m Factor	p. 282
e^jwt Factor	p. 284
Shift Operator Calculus	p. 287
Forward- and Backward-shift Operator	p. 287
Pulse Transfer Operator	p. 289
Recursive Least-squares Derivation	p. 293
Least-squares Method	p. 293
Equivalent Recursive Form	p. 294
Dissolved Oxygen Data	p. 297
References	p. 303
Index	p. 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
Publisher: SPRINGER VERLAG GMBH
Country of Publication: GB
Dimensions (cm): 23.11 x 15.49 x 2.29
Weight (kg): 0.5