{"product_id":"mastering-system-identification-in-100-exercises-9780470936986","title":"Mastering System Identification in 100 Exercises","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003eAbbreviations xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Identification 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Illustration of Some Important Aspects of System Identification 2\u003c\/p\u003e \u003cp\u003eExercise 1 .a (Least squares estimation of the value of a resistor) 2\u003c\/p\u003e \u003cp\u003eExercise 1 .b (Analysis of the standard deviation) 3\u003c\/p\u003e \u003cp\u003eExercise 2 (Study of the asymptotic distribution of an estimate) 5\u003c\/p\u003e \u003cp\u003eExercise 3 (Impact of noise on the regressor (input) measurements) 6\u003c\/p\u003e \u003cp\u003eExercise 4 (Importance of the choice of the independent variable or input) 7\u003c\/p\u003e \u003cp\u003eExercise 5.a (combining measurements with a varying SNR: Weighted least squares estimation) 8\u003c\/p\u003e \u003cp\u003eExercise 5.b (Weighted least squares estimation: A study of the variance) 9\u003c\/p\u003e \u003cp\u003eExercise 6 (Least squares estimation of models that are linear in the parameters) 11\u003c\/p\u003e \u003cp\u003eExercise 7 (Characterizing a 2-dimensional parameter estimate) 12\u003c\/p\u003e \u003cp\u003e1.3 Maximum Likelihood Estimation for Gaussian and Laplace Distributed Noise 14\u003c\/p\u003e \u003cp\u003eExercise 8 (Dependence of the optimal cost function on the distribution of the disturbing noise) 14\u003c\/p\u003e \u003cp\u003e1.4 Identification for Skew Distributions with Outliers 16\u003c\/p\u003e \u003cp\u003eExercise 9 (Identification in the presence of outliers) 16\u003c\/p\u003e \u003cp\u003e1.5 Selection of the Model Complexity 18\u003c\/p\u003e \u003cp\u003eExercise 10 (Influence of the number of parameters on the model uncertainty) 18\u003c\/p\u003e \u003cp\u003eExercise 11 (Model selection using the AIC criterion) 20\u003c\/p\u003e \u003cp\u003e1.6 Noise on Input and Output Measurements: The IV Method and the EIV Method 22\u003c\/p\u003e \u003cp\u003eExercise 12 (Noise on input and output: The instrumental variables method applied on the resistor estimate) 23\u003c\/p\u003e \u003cp\u003eExercise 13 (Noise on input and output: the errors-in-variables method) 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Generation and Analysis of Excitation Signals 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 The Discrete Fourier Transform (DFT) 30\u003c\/p\u003e \u003cp\u003eExercise 14 (Discretization in time: Choice of the sampling frequency: ALIAS) 31\u003c\/p\u003e \u003cp\u003eExercise 15 (Windowing: Study of the leakage effect and the frequency resolution) 31\u003c\/p\u003e \u003cp\u003e2.3 Generation and Analysis of Multisines and Other Periodic Signals 33\u003c\/p\u003e \u003cp\u003eExercise 16 (Generate a sine wave, noninteger number of periods measured) 34\u003c\/p\u003e \u003cp\u003eExercise 17 (Generate a sine wave, integer number of periods measured) 34\u003c\/p\u003e \u003cp\u003eExercise 18 (Generate a sine wave, doubled measurement time) 35\u003c\/p\u003e \u003cp\u003eExercise 19.a (Generate a sine wave using the MATLAB IFFT instruction) 37\u003c\/p\u003e \u003cp\u003eExercise 19.b (Generate a sine wave using the MATLAB IFFT instruction, defining only the first half of the spectrum) 37\u003c\/p\u003e \u003cp\u003eExercise 20 (Generation of a multisine with flat amplitude spectrum) 38\u003c\/p\u003e \u003cp\u003eExercise 21 (The swept sine signal) 39\u003c\/p\u003e \u003cp\u003eExercise 22.a (Spectral analysis of a multisine signal, leakage present) 40\u003c\/p\u003e \u003cp\u003eExercise 22.b (Spectral analysis of a multisine signal, no leakage present) 40\u003c\/p\u003e \u003cp\u003e2.4 Generation of Optimized Periodic Signals 42\u003c\/p\u003e \u003cp\u003eExercise 23 (Generation of a multisine with a reduced crest factor using random phase generation) 42\u003c\/p\u003e \u003cp\u003eExercise 24 (Generation of a multisine with a minimal crest factor using a crest factor minimization algorithm) 42\u003c\/p\u003e \u003cp\u003eExercise 25 (Generation of a maximum length binary sequence) 45\u003c\/p\u003e \u003cp\u003eExercise 26 (Tuning the parameters of a maximum length binary sequence) 46\u003c\/p\u003e \u003cp\u003e2.5 Generating Signals Using The Frequency Domain Identification Toolbox (FDIDENT) 46\u003c\/p\u003e \u003cp\u003eExercise 27 (Generation of excitation signals using the FDIDENT toolbox) 47\u003c\/p\u003e \u003cp\u003e2.6 Generation of Random Signals 48\u003c\/p\u003e \u003cp\u003eExercise 28 (Repeated realizations of a white random noise excitation with fixed length) 48\u003c\/p\u003e \u003cp\u003eExercise 29 (Repeated realizations of a white random noise excitation with increasing length) 49\u003c\/p\u003e \u003cp\u003eExercise 30 (Smoothing the amplitude spectrum of a random excitation) 49\u003c\/p\u003e \u003cp\u003eExercise 31 (Generation of random noise excitations with a user-imposed power spectrum) 50\u003c\/p\u003e \u003cp\u003eExercise 32 (Amplitude distribution of filtered noise) 51\u003c\/p\u003e \u003cp\u003e2.7 Differentiation, Integration, Averaging, and Filtering of Periodic Signals 52\u003c\/p\u003e \u003cp\u003eExercise 33 (Exploiting the periodic nature of signals: Differentiation, integration, +averaging, and filtering) 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 FRF Measurements 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 55\u003c\/p\u003e \u003cp\u003e3.2 Definition of the FRF 56\u003c\/p\u003e \u003cp\u003e3.3 FRF Measurements without Disturbing Noise 57\u003c\/p\u003e \u003cp\u003eExercise 34 (Impulse response function measurements) 57\u003c\/p\u003e \u003cp\u003eExercise 35 (Study of the sine response of a linear system: transients and steady-state) 58\u003c\/p\u003e \u003cp\u003eExercise 36 (Study of a multisine response of a linear system: transients and steady-state) 59\u003c\/p\u003e \u003cp\u003eExercise 37 (FRF measurement using a noise excitation and a rectangular window) 61\u003c\/p\u003e \u003cp\u003eExercise 38 (Revealing the nature of the leakage effect in FRF measurements) 61\u003c\/p\u003e \u003cp\u003eExercise 39 (FRF measurement using a noise excitation and a Hanning window) 64\u003c\/p\u003e \u003cp\u003eExercise 40 (FRF measurement using a noise excitation and a diff window) 65\u003c\/p\u003e \u003cp\u003eExercise 41 (FRF measurements using a burst excitation) 66\u003c\/p\u003e \u003cp\u003e3.4 FRF Measurements in the Presence of Disturbing Output Noise 68\u003c\/p\u003e \u003cp\u003eExercise 42 (Impulse response function measurements in the presence of output noise) 69\u003c\/p\u003e \u003cp\u003eExercise 43 (Measurement of the FRF using a random noise sequence and a random phase multisine in the presence of output noise) 70\u003c\/p\u003e \u003cp\u003eExercise 44 (Analysis of the noise errors on FRF measurements) 71\u003c\/p\u003e \u003cp\u003eExercise 45 (Impact of the block (period) length on the uncertainty) 73\u003c\/p\u003e \u003cp\u003e3.5 FRF Measurements in the Presence of Input and Output Noise 75\u003c\/p\u003e \u003cp\u003eExercise 46 (FRF measurement in the presence of input\/output disturbances using a multisine excitation) 75\u003c\/p\u003e \u003cp\u003eExercise 47 (Measuring the FRF in the presence of input and output noise: Analysis of the errors) 75\u003c\/p\u003e \u003cp\u003eExercise 48 (Measuring the FRF in the presence of input and output noise: Impact of the block (period) length on the uncertainty) 76\u003c\/p\u003e \u003cp\u003e3.6 FRF Measurements of Systems Captured in a Feedback Loop 78\u003c\/p\u003e \u003cp\u003eExercise 49 (Direct measurement of the FRF under feedback conditions) 78\u003c\/p\u003e \u003cp\u003eExercise 50 (The indirect method) 80\u003c\/p\u003e \u003cp\u003e3.7 FRF Measurements Using Advanced Signal Processing Techniques: The LPM 82\u003c\/p\u003e \u003cp\u003eExercise 51 (The local polynomial method) 82\u003c\/p\u003e \u003cp\u003eExercise 52 (Estimation of the power spectrum of the disturbing noise) 84\u003c\/p\u003e \u003cp\u003e3.8 Frequency Response Matrix Measurements for MIMO Systems 85\u003c\/p\u003e \u003cp\u003eExercise 53 (Measuring the FRM using multisine excitations) 85\u003c\/p\u003e \u003cp\u003eExercise 54 (Measuring the FRM using noise excitations) 86\u003c\/p\u003e \u003cp\u003eExercise 55 (Estimate the variance of the measured FRM) 88\u003c\/p\u003e \u003cp\u003eExercise 56 (Comparison of the actual and theoretical variance of the estimated FRM) 88\u003c\/p\u003e \u003cp\u003eExercise 57 (Measuring the FRM using noise excitations and a Hanning window) 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Identification of Linear Dynamic Systems 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 91\u003c\/p\u003e \u003cp\u003e4.2 Identification Methods that Are Linear-in-the-Parameters. The Noiseless Setup 93\u003c\/p\u003e \u003cp\u003eExercise 58 (Identification in the time domain) 94\u003c\/p\u003e \u003cp\u003eExercise 59 (Identification in the frequency domain) 96\u003c\/p\u003e \u003cp\u003eExercise 60 (Numerical conditioning) 97\u003c\/p\u003e \u003cp\u003eExercise 61 (Simulation and one-step-ahead prediction) 99\u003c\/p\u003e \u003cp\u003eExercise 62 (Identify a too-simple model) 100\u003c\/p\u003e \u003cp\u003eExercise 63 (Sensitivity of the simulation and prediction error to model errors) 101\u003c\/p\u003e \u003cp\u003eExercise 64 (Shaping the model errors in the time domain: Prefiltering) 102\u003c\/p\u003e \u003cp\u003eExercise 65 (Shaping the model errors in the frequency domain: frequency weighting) 102\u003c\/p\u003e \u003cp\u003e4.3 Time domain Identification using parametric noise models 104\u003c\/p\u003e \u003cp\u003eExercise 66 (One-step-ahead prediction of a noise sequence) 105\u003c\/p\u003e \u003cp\u003eExercise 67 (Identification in the time domain using parametric noise models) 108\u003c\/p\u003e \u003cp\u003eExercise 68 (Identification Under Feedback Conditions Using Time Domain Methods) 109\u003c\/p\u003e \u003cp\u003eExercise 69 (Generating uncertainty bounds for estimated models) 111\u003c\/p\u003e \u003cp\u003eExercise 70 (Study of the behavior of the BJ model in combination with prefiltering) 113\u003c\/p\u003e \u003cp\u003e4.4 Identification Using Nonparametric Noise Models and Periodic Excitations 115\u003c\/p\u003e \u003cp\u003eExercise 71 (Identification in the frequency domain using nonparametric noise models) 117\u003c\/p\u003e \u003cp\u003eExercise 72 (Emphasizing a frequency band) 119\u003c\/p\u003e \u003cp\u003eExercise 73 (Comparison of the time and frequency domain identification under feedback) 120\u003c\/p\u003e \u003cp\u003e4.5 Frequency Domain Identification Using Nonparametric Noise Models and Random Excitations 122\u003c\/p\u003e \u003cp\u003eExercise 74 (Identification in the frequency domain using nonparametric noise models and a random excitation) 122\u003c\/p\u003e \u003cp\u003e4.6 Time Domain Identification Using the System Identification Toolbox 123\u003c\/p\u003e \u003cp\u003eExercise 75 (Using the time domain identification toolbox) 124\u003c\/p\u003e \u003cp\u003e4.7 Frequency Domain Identification Using the Toolbox FDIDENT 129\u003c\/p\u003e \u003cp\u003eExercise 76 (Using the frequency domain identification toolbox FDIDENT) 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Best Linear Approximation of Nonlinear Systems 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Response of a nonlinear system to a periodic input 137\u003c\/p\u003e \u003cp\u003eExercise 77.a (Single sine response of a static nonlinear system) 138\u003c\/p\u003e \u003cp\u003eExercise 77.b (Multisine response of a static nonlinear system) 139\u003c\/p\u003e \u003cp\u003eExercise 78 (Uniform versus Pointwise Convergence) 142\u003c\/p\u003e \u003cp\u003eExercise 79.a (Normal operation, subharmonics, and chaos) 143\u003c\/p\u003e \u003cp\u003eExercise 79.b (Influence initial conditions) 146\u003c\/p\u003e \u003cp\u003eExercise 80 (Multisine response of a dynamic nonlinear system) 147\u003c\/p\u003e \u003cp\u003eExercise 81 (Detection, quantification, and classification of nonlinearities) 148\u003c\/p\u003e \u003cp\u003e5.2 Best Linear Approximation of a Nonlinear System 150\u003c\/p\u003e \u003cp\u003eExercise 82 (Influence DC values signals on the linear approximation) 151\u003c\/p\u003e \u003cp\u003eExercise 83.a (Influence of rms value and pdf on the BLA) 152\u003c\/p\u003e \u003cp\u003eExercise 83.b (Influence of power spectrum coloring and pdf on the BLA) 154\u003c\/p\u003e \u003cp\u003eExercise 83.c (Influence of length of impulse response of signal filter on the BLA) 156\u003c\/p\u003e \u003cp\u003eExercise 84.a (Comparison of Gaussian noise and random phase multisine) 158\u003c\/p\u003e \u003cp\u003eExercise 84.b (Amplitude distribution of a random phase multisine) 160\u003c\/p\u003e \u003cp\u003eExercise 84.c (Influence of harmonic content multisine on BLA) 162\u003c\/p\u003e \u003cp\u003eExercise 85 (Influence of even and odd nonlinearities on BLA) 165\u003c\/p\u003e \u003cp\u003eExercise 86 (BLA of a cascade) 167\u003c\/p\u003e \u003cp\u003e5.3 Predictive Power of The Best Linear Approximation 172\u003c\/p\u003e \u003cp\u003eExercise 87.a (Predictive power BLA — static NL system) 172\u003c\/p\u003e \u003cp\u003eExercise 87.b (Properties of output residuals — dynamic NL system) 174\u003c\/p\u003e \u003cp\u003eExercise 87.c (Predictive power of BLA — dynamic NL system) 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Measuring the Best Linear Approximation of a Nonlinear System 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Measuring the Best Linear Approximation 183\u003c\/p\u003e \u003cp\u003eExercise 88.a (Robust method for noisy FRF measurements) 186\u003c\/p\u003e \u003cp\u003eExercise 88.b (Robust method for noisy input\/output measurements without reference signal) 190\u003c\/p\u003e \u003cp\u003eExercise 88.c (Robust method for noisy input\/output measurements with reference signal) 195\u003c\/p\u003e \u003cp\u003eExercise 89.a (Design of baseband odd and full random phase multisines with random harmonic grid) 197\u003c\/p\u003e \u003cp\u003eExercise 89.b (Design of bandpass odd and full random phase multisines with random harmonic grid) 197\u003c\/p\u003e \u003cp\u003eExercise 89.c (Fast method for noisy input\/output measurements — open loop example) 203\u003c\/p\u003e \u003cp\u003eExercise 89.d (Fast method for noisy input\/output measurements — closed loop example) 207\u003c\/p\u003e \u003cp\u003eExercise 89.e (Bias on the estimated odd and even distortion levels) 211\u003c\/p\u003e \u003cp\u003eExercise 90 (Indirect method for measuring the best linear approximation) 215\u003c\/p\u003e \u003cp\u003eExercise 91 (Comparison robust and fast methods) 216\u003c\/p\u003e \u003cp\u003eExercise 92 (Confidence intervals for the BLA) 219\u003c\/p\u003e \u003cp\u003eExercise 93 (Prediction of the bias contribution in the BLA) 221\u003c\/p\u003e \u003cp\u003eExercise 94 (True underlying linear system) 222\u003c\/p\u003e \u003cp\u003e6.2 Measuring the nonlinear distortions 224\u003c\/p\u003e \u003cp\u003eExercise 95 (Prediction of the nonlinear distortions using random harmonic grid multisines) 225\u003c\/p\u003e \u003cp\u003eExercise 96 (Pros and cons full-random and odd-random multisines) 230\u003c\/p\u003e \u003cp\u003e6.3 Guidelines 233\u003c\/p\u003e \u003cp\u003e6.4 Projects 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Identification of Parametric Models in the Presence of Nonlinear Distortions 239\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 239\u003c\/p\u003e \u003cp\u003e7.2 Identification of the Best Linear Approximation Using Random Excitations 240\u003c\/p\u003e \u003cp\u003eExercise 97 (Parametric estimation of the best linear approximation) 240\u003c\/p\u003e \u003cp\u003e7.3 Generation of Uncertainty Bounds? 243\u003c\/p\u003e \u003cp\u003eExercise 98 243\u003c\/p\u003e \u003cp\u003e7.4 Identification of the best linear approximation using periodic excitations 245\u003c\/p\u003e \u003cp\u003eExercise 99 (Estimate a parametric model for the best linear approximation using the Fast Method) 246\u003c\/p\u003e \u003cp\u003eExercise 100 (Estimating a parametric model for the best linear approximation using the robust method) 251\u003c\/p\u003e \u003cp\u003e7.5 Advises and conclusions 252\u003c\/p\u003e \u003cp\u003eReferences 255\u003c\/p\u003e \u003cp\u003eSubject Index 259\u003c\/p\u003e \u003cp\u003eReference Index 263\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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