Adaptive filter theory
Author(s)
Bibliographic Information
Adaptive filter theory
（Prentice-Hall information and system sciences series）
Prentice-Hall, c1996
3rd ed
- pbk
Related Bibliography 1 items
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Adaptive filter theory / Simon Haykin
BA40072962
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Adaptive filter theory / Simon Haykin
Available at 34 libraries
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Kanazawa University Library研究室
: pbk549.3:H4199908-50634-X,
549.3:H4199608-51343-X,9608-51409-6,9708-51269-9,9808-50219-9
Note
Bibliography: p. 941-977
Includes index
Description and Table of Contents
- Volume
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ISBN 9780133227604
Description
Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. This edition has been updated and refined to keep current with the field and develop concepts in as unified and accessible a manner as possible. It: introduces a completely new chapter on Frequency-Domain Adaptive Filters; adds a chapter on Tracking Time-Varying Systems; adds two chapters on Neural Networks; enhances material on RLS algorithms; strengthens linkages to Kalman filter theory to gain a more unified treatment of the standard, square-root and order-recursive forms; and includes new computer experiments using MATLAB software that illustrate the underlying theory and applications of the LMS and RLS algorithms.
Table of Contents
BACKGROUND MATERIAL. Discrete-Time Signal Processing. Stationary Processes and Models. Spectrum Analysis. Eigenanalysis. LINEAR OPTIMUM FILTERING. Wiener Filters. Linear Prediction. Kalman Filters. LINEAR ADAPTIVE FILTERING. Method of Steepest Descent. Least-Mean Square Algorithm. Frequency-Domain Adaptive Filters. Method of Least Squares. Rotations and Reflections. Recursive Least-Squares Algorithm. Square-Root Adaptive Filtering. Order-Recursive Adaptive Filters. Tracking of Time-Varying Systems. Finite-Precision Effects. NONLINEAR ADAPTIVE FILTERING. Blind Deconvolution. Back-Propagation Learning. Radial Basis Function Networks.
- Volume
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pbk ISBN 9780133979855
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"