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ISBNSpecification
  SPECIFICATION
ADAPTIVE FILTER THEORY 4e(pb)2008
Author: SIMON HAYKIN
ISBN: 9788131708699
Year: 2008
Publisher: PEARSON EDUCATION ASIA
Category: ELECTRICAL ENGINEERING
Edition: 4
Format: Paperback
Language: English
Pages: 920
 ABOUT THE TITLE
The subject of adaptive filters constitutes an important part of statistical signal processing. Whenever there is a requirement to process signals that result from operation in an environment of unknown statistics or one that is inherently nonstationary the use of an adaptive filter offers a highly attractive solution to the problem as it provides a significant improvement in performance over the use of a fixed filter designed by conventional methods. The primary aim of this book is to develop the mathematical theory of various realizations of linear adaptive filtersI. Adaptation is accomplished by adjusting the free parameters (coefficients) of a filter in accordance with the input data which in reality makes the adaptive filter nonlinear. When we speak of an adaptive filter being linear we mean the following: The input-output map of the filter obeys the principle of superposition whenever at any particular instant of time the filters parameters are all fixed.
Salient Features:
• Improves the presentation of material on statistical LMS theory and statistical RLS theory.
• Expands the treatment of normalized LMS filters and introduces the more general case of affine projection filters.
• Introduces sub-band adaptive filters.
• Repositions the teaching of Kalman filters after the treatment of RLS filters thereby enhancing the unified treatment of square-root adaptive filters and order recursive adaptive filters.
• In-depth treatment of adaptive filters in a highly readable and understandable fashion.
• Major revision of the MATLAB codes for the computer experiments—Available on the web.
• Extensive use of MATLAB experiments—Illustrates the practical realities and intricacies of adaptive filters the codes for which can be downloaded from the Web.
Author:
Simon Haykin McMaster University Ontario Canada
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 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.
Adaptive Filters [CORE TEXTS] (Electrical and Computing Engineering)
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