Adaptive Filter Theory by Simon Haykin

Introduction:
Adaptive Filter Theory looks at both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. Up-to-date and in-depth treatment of adaptive filters develops concepts in a unified and accessible manner. This highly successful book provides comprehensive coverage of adaptive filters in a highly readable and understandable fashion. Includes an extensive use of illustrative examples; and MATLAB experiments, which illustrate the practical realities and intricacies of adaptive filters, the codes for which can be downloaded from the Web. Covers a wide range of topics including Stochastic Processes, Wiener Filters, and Kalman Filters. For those interested in learning about adaptive filters and the theories behind them.

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.

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