Mastering System Identification in 100 Exercises

Informationen zum Autor Johan Schoukens , PhD, serves as a full-time professor in the ELEC Department at the Vrije Universiteit Brussel. He has been a Fellow of IEEE since 1997 and was the recipient of the 2003 IEEE Instrumentation and Measurement Society Distinguished Service Award. Rik Pintelon , PhD, serves as a full-time professor at the Vrije Universiteit Brussel in the ELEC Department. He has been a Fellow of IEEE since 1998 and is the recipient of the 2012 IEEE Joseph F. Keithley Award in Instrumentation and Measurement (IEEE Technical Field Award). Yves Rolain , PhD, serves as a full-time professor at the Vrije Universiteit Brussel in the ELEC department. He has been a Fellow of IEEE since 2006 and was the recipient of the 2004 IEEE Instrumentation and Measurement Society Technical Award. Klappentext Systems identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Mastering System Identification in 100 Exercises takes readers step by step through a series of MATLAB exercises that teach how to measure and model linear dynamic systems in the presence of nonlinear distortions from a practical point of view. Each exercise is followed by a short discussion illustrating what lessons can be learned by the reader.The book, with its learn-by-doing approach, also includes:* State-of-the-art system identification methods, with both time and frequency domain system identification methods--including the pros and cons of each* Simple writing style with numerous examples and figures* Downloadable author-programmed MATLAB files for each exercise--with detailed solutions* Larger projects that serve as potential assignmentsCovering both classic and recent measurement and identifying methods, this book will appeal to practicing engineers, scientists, and researchers, as well as master's and PhD students in electrical, mechanical, civil, and chemical engineering. Zusammenfassung This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource. Inhaltsverzeichnis Preface xiii Acknowledgments xv Abbreviations xvii 1 Identification 1 1.1 Introduction 1 1.2 Illustration of Some Important Aspects of System Identification 2 Exercise 1 .a (Least squares estimation of the value of a resistor) 2 Exercise 1 .b (Analysis of the standard deviation) 3 Exercise 2 (Study of the asymptotic distribution of an estimate) 5 Exercise 3 (Impact of noise on the regressor (input) measurements) 6 Exercise 4 (Importance of the choice of the independent variable or input) 7 Exercise 5.a (combining measurements with a varying SNR: Weighted least squares estimation) 8 Exercise 5.b (Weighted least squares estimation: A study of the variance) 9 Exercise 6 (Least squares estimation of models that are linear in the parameters) 11 Exercise 7 (Characterizing a 2-dimensional parameter estimate) 12 1.3 Maximum Likelihood Estimation for Gaussian and Laplace Distributed Noise 14 Exercise 8 (Dependence of the optimal cost function on the distribution of the disturbing noise) 14 1.4 Identification for Skew Distributions with Outliers 16 Exercise 9 (Identification in the presence of outliers) 16 1.5 Selection of the Model Complexity 18 Exercise 10 (Influence of the number of parameters on the model uncerta...

List of contents

Preface xiii
 
Acknowledgments xv
 
Abbreviations xvii
 
1 Identification 1
 
Exercise 1 .a (Least squares estimation of the value of a resistor) 2
 
Exercise 1 .b (Analysis of the standard deviation) 3
 
Exercise 2 (Study of the asymptotic distribution of an estimate) 5
 
Exercise 3 (Impact of noise on the regressor (input) measurements) 6
 
Exercise 4 (Importance of the choice of the independent variable or input) 7
 
Exercise 5.a (combining measurements with a varying SNR: Weighted least squares estimation) 8
 
Exercise 5.b (Weighted least squares estimation: A study of the variance) 9
 
Exercise 6 (Least squares estimation of models that are linear in the parameters) 11
 
Exercise 7 (Characterizing a 2-dimensional parameter estimate) 12
 
Exercise 8 (Dependence of the optimal cost function on the distribution of the disturbing noise) 14
 
Exercise 9 (Identification in the presence of outliers) 16
 
Exercise 10 (Influence of the number of parameters on the model uncertainty) 18
 
Exercise 11 (Model selection using the AIC criterion) 20
 
Exercise 12 (Noise on input and output: The instrumental variables method applied on the resistor estimate) 23
 
Exercise 13 (Noise on input and output: the errors-in-variables method) 25
 
2 Generation and Analysis of Excitation Signals 29
 
Exercise 14 (Discretization in time: Choice of the sampling frequency: ALIAS) 31
 
Exercise 15 (Windowing: Study of the leakage effect and the frequency resolution) 31
 
Exercise 16 (Generate a sine wave, noninteger number of periods measured) 34
 
Exercise 17 (Generate a sine wave, integer number of periods measured) 34
 
Exercise 18 (Generate a sine wave, doubled measurement time) 35
 
Exercise 19.a (Generate a sine wave using the MATLAB IFFT instruction) 37
 
Exercise 19.b (Generate a sine wave using the MATLAB IFFT instruction, defining only the first half of the spectrum) 37
 
Exercise 20 (Generation of a multisine with flat amplitude spectrum) 38
 
Exercise 21 (The swept sine signal) 39
 
Exercise 22.a (Spectral analysis of a multisine signal, leakage present) 40
 
Exercise 22.b (Spectral analysis of a multisine signal, no leakage present) 40
 
Exercise 23 (Generation of a multisine with a reduced crest factor using random phase generation) 42
 
Exercise 24 (Generation of a multisine with a minimal crest factor using a crest factor minimization algorithm) 42
 
Exercise 25 (Generation of a maximum length binary sequence) 45
 
Exercise 26 (Tuning the parameters of a maximum length binary sequence) 46
 
Exercise 27 (Generation of excitation signals using the FDIDENT toolbox) 47
 
Exercise 28 (Repeated realizations of a white random noise excitation with fixed length) 48
 
Exercise 29 (Repeated realizations of a white random noise excitation with increasing length) 49
 
Exercise 30 (Smoothing the amplitude spectrum of a random excitation) 49
 
Exercise 31 (Generation of random noise excitations with a user-imposed power spectrum) 50
 
Exercise 32 (Amplitude distribution of filtered noise) 51
 
Exercise 33 (Exploiting the periodic nature of signals: Differentiation, integration, +averaging, and filtering) 52
 
3 FRF Measurements 55
 
Exercise 34 (Impulse response function measurements) 57
 
Exercise 35 (Study of the sine response of a linear system: transients and steady-state) 58
 
Exercise 36 (Study of a multisine response of a linear system: transients and steady-state) 59
 
Exercise 37 (FRF measurement using a noise excitation and a rectangular window

Product details

• Authors: Pintelon, Ri Pintelon, Rik Pintelon, Pintelon Rik, Rolain, Yves Rolain, Rolain Yves, Schoukens, J Schoukens, Joha Schoukens, Johan Schoukens, Johan (Department of Electrical Enginee Schoukens, Johan Pintelon Schoukens, Johan/ Pintelon Schoukens, Schoukens Johan, SCHOUKENS JOHAN PINTELON RIK ROL
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 20.04.2012
• EAN: 9780470936986
• ISBN: 978-0-470-93698-6
• No. of pages: 282
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Maschinenbau, Regelungstechnik, Signalverarbeitung, Mechanical Engineering, Signal Processing, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Mess- u. Regeltechnik, Control Process & Measurements, Control Systems Technology