Adaptive filter theory
Author(s)
Bibliographic Information
Adaptive filter theory
(Prentice-Hall information and system sciences series)
Prentice-Hall, c1996
3rd ed, Prentice Hall international editions
- : pbk.
Related Bibliography 1 items
-
-
Adaptive filter theory / Simon Haykin
BA27959392
-
Adaptive filter theory / Simon Haykin
Available at 6 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Bibliography: p. 941-977
Includes index
Description and Table of Contents
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.
Table of Contents
(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.
by "Nielsen BookData"