Synopses & Reviews
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
- 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
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.
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.
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.