Simon Haykin

Adaptive Filter Theory

International Edition

• Broschiertes Buch

Produktbeschreibung

For courses in Adaptive Filters. Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.

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Produktdetails

• Verlag: Pearson Education Limited
• 5 ed
• Seitenzahl: 912
• Erscheinungstermin: 2. August 2013
• Englisch
• Abmessung: 233mm x 177mm x 45mm
• Gewicht: 1544g
• ISBN-13: 9780273764083
• ISBN-10: 027376408X
• Artikelnr.: 35936351

Autorenporträt

Simon Haykin received his B.Sc. (First-class Honours), Ph.D., and D.Sc., all in Electrical Engineering from the University of Birmingham, England. He is a Fellow of the Royal Society of Canada, and a Fellow of the Institute of Electrical and Electronics Engineers. He is the recipient of the Henry Booker Gold Medal from URSI, 2002, the Honorary Degree of Doctor of Technical Sciences from ETH Zentrum, Zurich, Switzerland, 1999, and many other medals and prizes. He is a pioneer in adaptive signal-processing with emphasis on applications in radar and communications, an area of research which has occupied much of his professional life.

Inhaltsangabe

Chapter 1            Stochastic Processes and Models
Chapter 2            Wiener Filters
Chapter 3            Linear Prediction
Chapter 4            Method of Steepest Descent
Chapter 5            Method of Stochastic Gradient Descent
Chapter 6            The Least-Mean-Square (LMS) Algorithm
Chapter 7            Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization
Chapter 8            Block-Adaptive Filters
Chapter 9            Method of Least Squares
Chapter 10            The Recursive Least-Squares (RLS) Algorithm
Chapter 11            Robustness
Chapter 12            Finite-Precision Effects
Chapter 13            Adaptation in Nonstationary Environments
Chapter 14            Kalman Filters
Chapter 15            Square-Root Adaptive Filters
Chapter 16            Order-Recursive Adaptive Filters
Chapter 17            Blind Deconvolution