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