Adaptive filter theory / by Simon Haykin.

By: Haykin, Simon S, 1931-Material type: TextTextSeries: Prentice Hall information and system sciences seriesPublication details: Upper Saddle River, N.J. : Prentice Hall, c1996Edition: 3rd edDescription: vii, 989 p. : ill. ; 24 cmISBN: 013322760X(pbk)Subject(s): Adaptive filtersDDC classification: 621.3815
Contents:
CONTENTS Chapter 1: Discrete time signal processing Chapter 2: Stationary processes and models Chapter 3: spectrum analysis Chapter 4: Eigen-analysis Chapter 5: Wiener Fillers Chapter 6: Linear prediction Chapter 7: Kalman filters Chapter 8: Method of steepest descent Chapter 9: Least mean square algorithm Chapter 10: Frequency domain adoptive filters Chapter 11: Methods of least squares Chapter 12: Rotations and reflections Chapter 13: Recursive least squares algorithm Chapter 14: Square root adaptive filters Chapter 15: Order recursive adaptive filters Chapter 16: Tracking of time varying systems Chapter 17: Finite Precision effects Chapter 18: Blind deconvolution Chapter 19: Back Propagation learning Chapter 20: Radial bases function networks
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Item type Current library Call number Status Date due Barcode Item holds
Books Books Namal Library
Electrical Engineering
621.3815 HAY-A 1996 4558 (Browse shelf (Opens below)) Available 0004558
Total holds: 0

Includes bibliographical references (p. 941-977) and index.

CONTENTS
Chapter 1: Discrete time signal processing
Chapter 2: Stationary processes and models
Chapter 3: spectrum analysis
Chapter 4: Eigen-analysis
Chapter 5: Wiener Fillers
Chapter 6: Linear prediction
Chapter 7: Kalman filters
Chapter 8: Method of steepest descent
Chapter 9: Least mean square algorithm
Chapter 10: Frequency domain adoptive filters
Chapter 11: Methods of least squares
Chapter 12: Rotations and reflections
Chapter 13: Recursive least squares algorithm
Chapter 14: Square root adaptive filters
Chapter 15: Order recursive adaptive filters
Chapter 16: Tracking of time varying systems
Chapter 17: Finite Precision effects
Chapter 18: Blind deconvolution
Chapter 19: Back Propagation learning
Chapter 20: Radial bases function networks

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