(P/B) ADAPTIVE FILTER THEORY

Παρουσίαση

Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth 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. (From the publisher)

Περιεχόμενα

Preface
Background and Preview
1. The Filtering Problem
2. Linear Optimum Filters
3. Adaptive Filters
4. Linear Filter Structures
5. Approaches to the Development of Linear Adaptive Filters
6. Adaptive Beamforming
7. Four Classes of Applications
8. Historical Notes
Chapter 1. Stochastic Processes and Models
Chapter 2. Wiener Filters
Chapter 3. Linear Prediction
Chapter 4. Method of Steepest Descent
Chapter 5. Method of Stochastic Gradient Descent
Chapter 6. The Least-Mean-Square (LMS) Algorithm
Chapter 7. Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization
Chapter 8. Block-Adaptive Filters
Chapter 9. Method of Least Squares
Chapter 10. The Recursive Least-Squares (RLS) Algorithm
Chapter 11. Robustness
Chapter 12. Finite-Precision Effects
Chapter 13. Adaptation in Nonstationary Environments
Bibliography
Chapter 14. Kalman Filters
Chapter 15. Square-Root Adaptive Filters
Chapter 16. Order-Recursive Adaptive Filters
Chapter 17. Blind Deconvolution
Epilogue
1. Robusness, Efficiency, and Complexity
2. Kernel-Based Nonlinear Adaptive Filtering
Appendix A. Theory of Complex Variables
Appendix B. Computation of Derivatives in the Complex Domain
Appendix C. Method of Lagrange Multipliers
Appendix D. Estimation Theory
Appendix E. Eigenanalysis
Appendix F. Langevin Equation of Nonequilibrium Thermodynamics
Appendix G. Rotations and Reflections
Appendix H. Complex Wishart Distribution