Adaptive Filter Theory / Edition 4

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2001 Paperback Good 001 Item may show signs of shelf wear. Pages may include limited notes and highlighting. Includes supplemental or companion materials if applicable. Access ... codes may or may not work. Connecting readers since 1972. Customer service is our top priority. Read more Show Less

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Background and Preview

  • Chapter 1
    Stochastic Processes and Models
  • Chapter 2 Wiener Filters
  • Chapter 3 Linear Prediction
  • Chapter 4 Method of Steepest Descent
  • Chapter 5 Least-Mean-Square Adaptive Filters
  • Chapter 6 Normalized Least-Mean-Square Adaptive Filters
  • Chapter 7 Frequency-Domain and Subband Adaptive Filters
  • Chapter 8 Method of Least Squares
  • Chapter 9 Recursive Least-Square Adaptive Filters
  • Chapter 10 Kalman Filters
  • Chapter 11 Square-Root Adaptive Filters
  • Chapter 12 Order-Recursive Adaptive Filters
  • Chapter 13 Finite-Precision Effects
  • Chapter 14 Tracking of Time-Varying Systems
  • Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures
  • Chapter 16 Blind Deconvolution
  • Chapter 17 Back-Propagation Learning


  • Appendix A Complex Variables
  • Appendix B Differentiation with Respect to a Vector
  • Appendix C Method of Lagrange Multipliers
  • Appendix D Estimation Theory
  • Appendix E Eigenanalysis
  • Appendix F Rotations and Reflections
  • Appendix G Complex Wishart Distribution
  • Glossary
  • Bibliography
  • Index
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Editorial Reviews

At a level suitable for graduate courses on adaptive signal processing, this textbook develops the mathematical theory of various realizations of linear adaptive filters with finite-duration impulse response, and also provides an introductory treatment of supervised neural networks. Numerous computer experiments illustrate the underlying theory and applications of the LMS (least mean-square) and RLS (recursive-least-squares) algorithms, and problems conclude each chapter. Annotation c. Book News, Inc., Portland, OR (
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Product Details

  • ISBN-13: 9780130901262
  • Publisher: Prentice Hall
  • Publication date: 9/14/2001
  • Edition description: Subsequent
  • Edition number: 4
  • Pages: 936
  • Product dimensions: 7.00 (w) x 9.20 (h) x 1.90 (d)

Table of Contents

Background and Overview.

1. Stochastic Processes and Models.

2. Wiener Filters.

3. Linear Prediction.

4. Method of Steepest Descent.

5. Least-Mean-Square Adaptive Filters.

6. Normalized Least-Mean-Square Adaptive Filters.

7. Transform-Domain and Sub-Band Adaptive Filters.

8. Method of Least Squares.

9. Recursive Least-Square Adaptive Filters.

10. Kalman Filters as the Unifying Bases for RLS Filters.

11. Square-Root Adaptive Filters.

12. Order-Recursive Adaptive Filters.

13. Finite-Precision Effects.

14. Tracking of Time-Varying Systems.

15. Adaptive Filters Using Infinite-Duration Impulse Response Structures.

16. Blind Deconvolution.

17. Back-Propagation Learning.


Appendix A. Complex Variables.

Appendix B. Differentiation with Respect to a Vector.

Appendix C. Method of Lagrange Multipliers.

Appendix D. Estimation Theory.

Appendix E. Eigenanalysis.

Appendix F. Rotations and Reflections.

Appendix G. Complex Wishart Distribution.



Principal Symbols.



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