Adaptive Filter Theory
International Edition
Simon O. Haykin(Author)
Pearson (Publisher)
3rd Edition
Published on 17. January 1996
Book
Paperback/Softback
989 pages
978-0-13-397985-5 (ISBN)
Article exhausted; check for reprint
Description
Appropriate for graduate-level courses in Adaptive Signal Processing.
Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. The Third Edition of this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. The Third Edition of this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
More details
Edition
3rd edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 232 mm
Width: 178 mm
Thickness: 35 mm
Weight
1367 gr
ISBN-13
978-0-13-397985-5 (9780133979855)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions

Book
01/2004
4th Edition
Pearson
€61.89
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Content
(NOTE: Each chapter ends with Summary and Discussion, and Problems.)
Introduction.
I. BACKGROUND MATERIAL.
1. Discrete-Time Signal Processing.
2. Stationary Processes and Models.
3. Spectrum Analysis.
4. Eigenanalysis.
II. LINEAR OPTIMUM FILTERING.
5. Wiener Filters.
6. Linear Prediction.
7. Kalman Filters.
III. LINEAR ADAPTIVE FILTERING.
8. Method of Steepest Descent.
9. Least-Mean Square Algorithm.
10. Frequency-Domain Adaptive Filters.
11. Method of Least Squares.
12. Rotations and Reflections.
13. Recursive Least-Squares Algorithm.
14. Square-Root Adaptive Filters.
15. Order-Recursive Adaptive Filters.
16. Tracking of Time-Varying Systems.
17. Finite-Precision Effects.
IV. NONLINEAR ADAPTIVE FILTERING.
18. Blind Deconvolution.
19. Back-Propagation Learning.
20. Radial Basis Function Networks.
Appendix A: Complex Variables.
Appendix B: Differentiation with Respect to a Vector.
Appendix C: Method and Lagrange Multipliers.
Appendix D: Estimation Theory.
Appendix E: Maximum-Entropy Method.
Appendix F: Minimum-Variance Distortionless Response Spectrum.
Appendix G: Gradient Adaptive Lattice Algorithm.
Appendix H: Solution of the Difference Equation (9.75).
Appendix I: Steady-State Analysis of the LMS Algorithm without Invoking the Independence Assumption.
Appendix J: The Complex Wishart Distribution.
Glossary.
Abbreviations.
Principal Symbols.
Bibliograghy.
Index.
Introduction.
I. BACKGROUND MATERIAL.
1. Discrete-Time Signal Processing.
2. Stationary Processes and Models.
3. Spectrum Analysis.
4. Eigenanalysis.
II. LINEAR OPTIMUM FILTERING.
5. Wiener Filters.
6. Linear Prediction.
7. Kalman Filters.
III. LINEAR ADAPTIVE FILTERING.
8. Method of Steepest Descent.
9. Least-Mean Square Algorithm.
10. Frequency-Domain Adaptive Filters.
11. Method of Least Squares.
12. Rotations and Reflections.
13. Recursive Least-Squares Algorithm.
14. Square-Root Adaptive Filters.
15. Order-Recursive Adaptive Filters.
16. Tracking of Time-Varying Systems.
17. Finite-Precision Effects.
IV. NONLINEAR ADAPTIVE FILTERING.
18. Blind Deconvolution.
19. Back-Propagation Learning.
20. Radial Basis Function Networks.
Appendix A: Complex Variables.
Appendix B: Differentiation with Respect to a Vector.
Appendix C: Method and Lagrange Multipliers.
Appendix D: Estimation Theory.
Appendix E: Maximum-Entropy Method.
Appendix F: Minimum-Variance Distortionless Response Spectrum.
Appendix G: Gradient Adaptive Lattice Algorithm.
Appendix H: Solution of the Difference Equation (9.75).
Appendix I: Steady-State Analysis of the LMS Algorithm without Invoking the Independence Assumption.
Appendix J: The Complex Wishart Distribution.
Glossary.
Abbreviations.
Principal Symbols.
Bibliograghy.
Index.