Adaptive Filter Theory

Adaptive Filter Theory : United States Edition

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Adaptive Filter Theory, 4e, is ideal for courses in Adaptive Filters.

Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fourth edition, 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.
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Product details

  • Hardback | 936 pages
  • 176 x 234 x 38mm | 1,501.38g
  • Pearson
  • Upper Saddle River, NJ, United States
  • English
  • 4th edition
  • 0130901261
  • 9780130901262
  • 1,276,554

Back cover copy

"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 LearningEpilogueAppendix 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 "GlossaryBibliographyIndex "
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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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