Adaptive Filter Theory – Simon Haykin – 4th Edition

Description

For courses in Adaptive Filters. Simon Haykin examines both the mathematical theory behind various adaptive filters and the elements of supervised multilayer perceptrons. In its fourth edition, this highly successful has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.

  • Consolidates the mathematical treatment of linear adaptive filters.
  • Improves the presentation of material on statistical LMS theory and statistical RLS theory.
  • Expands the treatment of normalized LMS filters, and introduces the more general case of affine projection filters.
  • Introduces sub-band adaptive filters.
  • Repositions the teaching of Kalman filters after the treatment of RLS filters, thereby enhancing the unified treatment of square- adaptive filters and order recursive adaptive filters.

Table of Contents


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.
Epilogue.
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.
Abbreviations.
Principal Symbols.
Bibliography.
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