000			15859nam a2200973 i 4500
001			6183553
003			IEEE
005			20220712205826.0
006			m o d
007			cr |n|||||||||
008			151221s2012 njua ob 001 eng d
010			_z 2011038051 (print)
020			_a9781118218532 _qelectronic
020			_z9780470936986 _qpaperback
024	7		_a10.1002/9781118218532 _2doi
035			_a(CaBNVSL)mat06183553
035			_a(IDAMS)0b000064817eb43b
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA402 _b.S3556 2012eb
082	0	0	_a519.5 _223
100	1		_aSchoukens, J., _q(Johan) _eauthor. _926461
245	1	0	_aMastering system identification in 100 exercises / _cJohan Schoukens, Rik Pintelon, Yves Rolain.
246	3		_aMastering system identification in one hundred exercises
264		1	_aPicataway : _bIEEE Press, _cc2012.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012]
300			_a1 PDF (xviii 264 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
504			_aIncludes bibliographical references and indexes.
505	0		_aPreface 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 uncertainty) 18 -- Exercise 11 (Model selection using the AIC criterion) 20 -- 1.6 Noise on Input and Output Measurements: The IV Method and the EIV Method 22 -- 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 -- 2.1 Introduction 29 -- 2.2 The Discrete Fourier Transform (DFT) 30 -- 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 -- 2.3 Generation and Analysis of Multisines and Other Periodic Signals 33.
505	8		_aExercise 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 -- 2.4 Generation of Optimized Periodic Signals 42 -- 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 -- 2.5 Generating Signals Using The Frequency Domain Identification Toolbox (FDIDENT) 46 -- Exercise 27 (Generation of excitation signals using the FDIDENT toolbox) 47 -- 2.6 Generation of Random Signals 48 -- 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 -- 2.7 Differentiation, Integration, Averaging, and Filtering of Periodic Signals 52 -- Exercise 33 (Exploiting the periodic nature of signals: Differentiation, integration, +averaging, and filtering) 52 -- 3 FRF Measurements 55 -- 3.1 Introduction 55.
505	8		_a3.2 Definition of the FRF 56 -- 3.3 FRF Measurements without Disturbing Noise 57 -- 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) 61 -- Exercise 38 (Revealing the nature of the leakage effect in FRF measurements) 61 -- Exercise 39 (FRF measurement using a noise excitation and a Hanning window) 64 -- Exercise 40 (FRF measurement using a noise excitation and a diff window) 65 -- Exercise 41 (FRF measurements using a burst excitation) 66 -- 3.4 FRF Measurements in the Presence of Disturbing Output Noise 68 -- Exercise 42 (Impulse response function measurements in the presence of output noise) 69 -- Exercise 43 (Measurement of the FRF using a random noise sequence and a random phase multisine in the presence of output noise) 70 -- Exercise 44 (Analysis of the noise errors on FRF measurements) 71 -- Exercise 45 (Impact of the block (period) length on the uncertainty) 73 -- 3.5 FRF Measurements in the Presence of Input and Output Noise 75 -- Exercise 46 (FRF measurement in the presence of input/output disturbances using a multisine excitation) 75 -- Exercise 47 (Measuring the FRF in the presence of input and output noise: Analysis of the errors) 75 -- Exercise 48 (Measuring the FRF in the presence of input and output noise: Impact of the block (period) length on the uncertainty) 76 -- 3.6 FRF Measurements of Systems Captured in a Feedback Loop 78 -- Exercise 49 (Direct measurement of the FRF under feedback conditions) 78 -- Exercise 50 (The indirect method) 80 -- 3.7 FRF Measurements Using Advanced Signal Processing Techniques: The LPM 82 -- Exercise 51 (The local polynomial method) 82 -- Exercise 52 (Estimation of the power spectrum of the disturbing noise) 84 -- 3.8 Frequency Response Matrix Measurements for MIMO Systems 85.
505	8		_aExercise 53 (Measuring the FRM using multisine excitations) 85 -- Exercise 54 (Measuring the FRM using noise excitations) 86 -- Exercise 55 (Estimate the variance of the measured FRM) 88 -- Exercise 56 (Comparison of the actual and theoretical variance of the estimated FRM) 88 -- Exercise 57 (Measuring the FRM using noise excitations and a Hanning window) 89 -- 4 Identification of Linear Dynamic Systems 91 -- 4.1 Introduction 91 -- 4.2 Identification Methods that Are Linear-in-the-Parameters. The Noiseless Setup 93 -- Exercise 58 (Identification in the time domain) 94 -- Exercise 59 (Identification in the frequency domain) 96 -- Exercise 60 (Numerical conditioning) 97 -- Exercise 61 (Simulation and one-step-ahead prediction) 99 -- Exercise 62 (Identify a too-simple model) 100 -- Exercise 63 (Sensitivity of the simulation and prediction error to model errors) 101 -- Exercise 64 (Shaping the model errors in the time domain: Prefiltering) 102 -- Exercise 65 (Shaping the model errors in the frequency domain: frequency weighting) 102 -- 4.3 Time domain Identification using parametric noise models 104 -- Exercise 66 (One-step-ahead prediction of a noise sequence) 105 -- Exercise 67 (Identification in the time domain using parametric noise models) 108 -- Exercise 68 (Identification Under Feedback Conditions Using Time Domain Methods) 109 -- Exercise 69 (Generating uncertainty bounds for estimated models) 111 -- Exercise 70 (Study of the behavior of the BJ model in combination with prefiltering) 113 -- 4.4 Identification Using Nonparametric Noise Models and Periodic Excitations 115 -- Exercise 71 (Identification in the frequency domain using nonparametric noise models) 117 -- Exercise 72 (Emphasizing a frequency band) 119 -- Exercise 73 (Comparison of the time and frequency domain identification under feedback) 120 -- 4.5 Frequency Domain Identification Using Nonparametric Noise Models and Random Excitations 122 -- Exercise 74 (Identification in the frequency domain using nonparametric noise models and a random excitation) 122.
505	8		_a4.6 Time Domain Identification Using the System Identification Toolbox 123 -- Exercise 75 (Using the time domain identification toolbox) 124 -- 4.7 Frequency Domain Identification Using the Toolbox FDIDENT 129 -- Exercise 76 (Using the frequency domain identification toolbox FDIDENT) 129 -- 5 Best Linear Approximation of Nonlinear Systems 137 -- 5.1 Response of a nonlinear system to a periodic input 137 -- Exercise 77.a (Single sine response of a static nonlinear system) 138 -- Exercise 77.b (Multisine response of a static nonlinear system) 139 -- Exercise 78 (Uniform versus Pointwise Convergence) 142 -- Exercise 79.a (Normal operation, subharmonics, and chaos) 143 -- Exercise 79.b (Influence initial conditions) 146 -- Exercise 80 (Multisine response of a dynamic nonlinear system) 147 -- Exercise 81 (Detection, quantification, and classification of nonlinearities) 148 -- 5.2 Best Linear Approximation of a Nonlinear System 150 -- Exercise 82 (Influence DC values signals on the linear approximation) 151 -- Exercise 83.a (Influence of rms value and pdf on the BLA) 152 -- Exercise 83.b (Influence of power spectrum coloring and pdf on the BLA) 154 -- Exercise 83.c (Influence of length of impulse response of signal filter on the BLA) 156 -- Exercise 84.a (Comparison of Gaussian noise and random phase multisine) 158 -- Exercise 84.b (Amplitude distribution of a random phase multisine) 160 -- Exercise 84.c (Influence of harmonic content multisine on BLA) 162 -- Exercise 85 (Influence of even and odd nonlinearities on BLA) 165 -- Exercise 86 (BLA of a cascade) 167 -- 5.3 Predictive Power of The Best Linear Approximation 172 -- Exercise 87.a (Predictive power BLA - static NL system) 172 -- Exercise 87.b (Properties of output residuals - dynamic NL system) 174 -- Exercise 87.c (Predictive power of BLA - dynamic NL system) 178 -- 6 Measuring the Best Linear Approximation of a Nonlinear System 183 -- 6.1 Measuring the Best Linear Approximation 183 -- Exercise 88.a (Robust method for noisy FRF measurements) 186.
505	8		_aExercise 88.b (Robust method for noisy input/output measurements without reference signal) 190 -- Exercise 88.c (Robust method for noisy input/output measurements with reference signal) 195 -- Exercise 89.a (Design of baseband odd and full random phase multisines with random harmonic grid) 197 -- Exercise 89.b (Design of bandpass odd and full random phase multisines with random harmonic grid) 197 -- Exercise 89.c (Fast method for noisy input/output measurements - open loop example) 203 -- Exercise 89.d (Fast method for noisy input/output measurements - closed loop example) 207 -- Exercise 89.e (Bias on the estimated odd and even distortion levels) 211 -- Exercise 90 (Indirect method for measuring the best linear approximation) 215 -- Exercise 91 (Comparison robust and fast methods) 216 -- Exercise 92 (Confidence intervals for the BLA) 219 -- Exercise 93 (Prediction of the bias contribution in the BLA) 221 -- Exercise 94 (True underlying linear system) 222 -- 6.2 Measuring the nonlinear distortions 224 -- Exercise 95 (Prediction of the nonlinear distortions using random harmonic grid multisines) 225 -- Exercise 96 (Pros and cons full-random and odd-random multisines) 230 -- 6.3 Guidelines 233 -- 6.4 Projects 233 -- 7 Identification of Parametric Models in the Presence of Nonlinear Distortions 239 -- 7.1 Introduction 239 -- 7.2 Identification of the Best Linear Approximation Using Random Excitations 240 -- Exercise 97 (Parametric estimation of the best linear approximation) 240 -- 7.3 Generation of Uncertainty Bounds? 243 -- Exercise 98 243 -- 7.4 Identification of the best linear approximation using periodic excitations 245 -- Exercise 99 (Estimate a parametric model for the best linear approximation using the Fast Method) 246 -- Exercise 100 (Estimating a parametric model for the best linear approximation using the robust method) 251 -- 7.5 Advises and conclusions 252 -- References 255 -- Subject Index 259 -- Reference Index 263.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_a"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"-- _cProvided by publisher.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/21/2015.
650		0	_aLinear systems _xMathematical models _xQuality control. _927959
650		0	_aSystem identification _vProblems, exercises, etc. _927960
655		0	_aElectronic books. _93294
695			_aAutoregressive processes
695			_aCost function
695			_aCurrent measurement
695			_aData models
695			_aDiscrete Fourier transforms
695			_aDistortion measurement
695			_aFrequency domain analysis
695			_aFrequency measurement
695			_aGaussian noise
695			_aGold
695			_aHarmonic analysis
695			_aHeating
695			_aIndexes
695			_aLeast squares approximation
695			_aLinear approximation
695			_aMathematical model
695			_aMeasurement uncertainty
695			_aNoise
695			_aNoise measurement
695			_aNonlinear distortion
695			_aNonlinear dynamical systems
695			_aNumerical models
695			_aPredictive models
695			_aSignal resolution
695			_aSteady-state
695			_aSystem identification
695			_aTime domain analysis
695			_aTime frequency analysis
695			_aTime measurement
695			_aTransient analysis
695			_aUncertainty
695			_aVoltage measurement
700	1		_aPintelon, R. _q(Rik) _927961
700	1		_aRolain, Yves. _927962
710	2		_aIEEE Xplore (Online Service), _edistributor. _927963
710	2		_aJohn Wiley & Sons, _epublisher. _96902
776	0	8	_iPrint version: _z9780470936986
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6183553
942			_cEBK
999			_c74228 _d74228