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Adaptive Filter Theory View Larger Image | Simon Haykin
Prentice Hall, Hardcover, 4th edition, Published September 2001, 920 pages, ISBN 0130901261 | List Price: $148.80
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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. KEY
TOPICS: 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. MARKET: For those interested in learning about
adaptive filters and the theories behind them.
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.
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.
Index.
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