Adaptive Filter Theory – Simon Haykin – 4th Edition

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.