Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser

More documents

Recommendations

Info

3.3. 1-D SYNTHETIC AND REAL DATA EXAMPLES 45 Samples Samples 0 20 40 60 80 100 0 20 40 60 80 100 (a) Amplitude −2 0 2 (d) Amplitude −2 0 2 Samples Samples 0 20 40 60 80 100 0 20 40 60 80 100 (b) Amplitude −2 0 2 (e) Amplitude −2 0 2 Samples Samples 0 20 40 60 80 100 0 20 40 60 80 100 (c) Amplitude −2 0 2 (f) Amplitude −2 0 2 Figure 3.7: Arctic Oscillation data for standardized nonlinear sea-level pressures. (a) Original data. (b) Prediction using a third-order Volterra series with parameters p = 10, q = 10, and r = 10. (c) Contribution from the linear part. (d) Contribution from the quadratic part. (e) Contribution from the cubic part. (f) Contribution from both quadratic and cubic parts (q = 10, and r = 10).
3.4. SUMMARY 46 3.4 Summary In this chapter, I have covered theoretical and practical aspects of linear and nonlinear systems; particularly, Volterra series and the nonlinear prediction model based on a third-order Volterra series was presented. Linear and nonlinear autoregressive models have been explored. Volterra kernel parameters are obtained via a least squares inversion method. Real and synthetic data examples illustrate these meth- ods. Linear and nonlinear time series modeling of 1-D data has been presented.
Page 1 and 2:
Name of Author: Soner Bekleric Univ
Page 3 and 4:
University of Alberta Faculty of Gr
Page 5 and 6:
Abstract Linear filter theory has p
Page 7 and 8:
Contents 1 Introduction 1 1.1 Appli
Page 9 and 10: List of Tables 3.1 Filter length an
Page 11 and 12: 4.4 (a) Prediction of Figure 4.2(b)
Page 13 and 14: List of symbols Symbol Name or desc
Page 15 and 16: List of abbreviations Abbreviation
Page 17 and 18: In applied seismology predictive de
Page 19 and 20: 1.1. APPLICATIONS 4 applied to vide
Page 21 and 22: 1.3. THESIS OUTLINE 6 • Chapter 4
Page 23 and 24: 2.2. LINEAR PROCESS 8 Finally, I pr
Page 25 and 26: 2.3. LINEAR PREDICTION 10 E(z) 1 X(
Page 27 and 28: 2.3. LINEAR PREDICTION 12 In my the
Page 29 and 30: 2.3. LINEAR PREDICTION 14 which can
Page 31 and 32: 2.3. LINEAR PREDICTION 16 ∂ρ fb
Page 33 and 34: 2.3. LINEAR PREDICTION 18 I have an
Page 35 and 36: 2.4. 1-D SYNTHETIC AND REAL DATA EX
Page 39 and 40: 2.6. SUMMARY 24 Relative PSD Relati
Page 41 and 42: Chapter 3 Nonlinear Prediction 3.1
Page 43 and 44: 3.1. NONLINEAR PROCESSES VIA THE VO
Page 45 and 46: 3.1. NONLINEAR PROCESSES VIA THE VO
Page 47 and 48: 3.2. NONLINEAR MODELING OF TIME SER
Page 59: 3.3. 1-D SYNTHETIC AND REAL DATA EX
Page 63 and 64: 4.1. LINEAR PREDICTION IN THE F −
Page 65 and 66: 4.2. ANALYSIS OF OPTIMUM FILTER LEN
Page 67 and 68: 4.2. ANALYSIS OF OPTIMUM FILTER LEN
Page 69 and 70: 4.3. NONLINEAR PREDICTION OF COMPLE
Page 73 and 74: RMSE 2 4.3. NONLINEAR PREDICTION OF
Page 77 and 78: 4.4. NOISE REMOVAL AND VOLTERRA SER
Page 79 and 80: 4.5. SYNTHETIC AND REAL DATA EXAMPL
Page 89 and 90: 4.6. SUMMARY 74 4.6 Summary In this
Page 91 and 92: 5.1. INTRODUCTION 76 ples remains a
Page 93 and 94: 5.3. SYNTHETIC DATA EXAMPLES 78 Not
Page 95 and 96: 5.3. SYNTHETIC DATA EXAMPLES 80 Tim
Page 97 and 98: 5.3. SYNTHETIC DATA EXAMPLES 82 Tim
Page 99 and 100: 5.4. REAL DATA EXAMPLES 84 Time [s]
Page 105 and 106: 5.5. SUMMARY 90 5.5 Summary In this
Page 107 and 108: series in this thesis has been trun
Page 109 and 110: 6.1. FUTURE DIRECTIONS 94 Noise red
Page 111 and 112:
BIBLIOGRAPHY 96 Canales, L. L. (198
Page 113 and 114:
BIBLIOGRAPHY 98 Marple, S. L. (1980
Page 115 and 116:
BIBLIOGRAPHY 100 Trad, D. (2003). I
show all

Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser

Create successful ePaper yourself

Delete template?

Save as template?