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Adaptive Filter Theory de Simon Haykin

Adaptive Filter Theory

Autor Simon Haykin
en Limba Engleză Paperback – 26 iul 2013
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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Specificații

ISBN-13: 9780273764083
ISBN-10: 027376408X
Pagini: 912
Ilustrații: Illustrations
Dimensiuni: 178 x 235 x 49 mm
Greutate: 1.55 kg
Ediția:5. Auflage
Editura: Pearson

Notă biografică

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.

Cuprins

  • 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