Synopses & Reviews
CONTENTS
Preface
Acknowledgments
Background and Preview
- Chapter 1 Stochastic Processes and Models
- Chapter 2 Wiener Filters
- Chapter 3 Linear Prediction
- Chapter 4 Method of Steepest Descent
- Chapter 5 Least-Mean-Square Adaptive Filters
- Chapter 6 Normalized Least-Mean-Square Adaptive Filters
- Chapter 7 Frequency-Domain and Subband Adaptive Filters
- Chapter 8 Method of Least Squares
- Chapter 9 Recursive Least-Square Adaptive Filters
- Chapter 10 Kalman Filters
- Chapter 11 Square-Root Adaptive Filters
- Chapter 12 Order-Recursive Adaptive Filters
- Chapter 13 Finite-Precision Effects
- Chapter 14 Tracking of Time-Varying Systems
- Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures
- Chapter 16 Blind Deconvolution
- Chapter 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
- Bibliography
- Index
Synopsis
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.
Description
Includes bibliographical references (p. 941-977) and index.
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.