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Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® de Alexander D. Poularikas

Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB®

Autor Alexander D. Poularikas
en Limba Engleză Paperback – 26 sep 2014
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area—the least mean square (LMS) adaptive filter.
This largely self-contained text:
  • Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions
  • Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces
  • Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton’s algorithm
  • Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples
  • Delivers a concise introduction to MATLAB®, supplying problems, computer experiments, and more than 110 functions and script files
Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.
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Specificații

ISBN-13: 9781482253351
ISBN-10: 1482253356
Pagini: 364
Ilustrații: 129 black & white illustrations, 19 black & white tables
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.54 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Notă biografică

Alexander D. Poularikas is chairman of the electrical and computer engineering department at the University of Alabama in Huntsville, USA. He previously held positions at University of Rhode Island, Kingston, USA and the University of Denver, Colorado, USA. He has published, coauthored, and edited 14 books and served as an editor-in-chief of numerous book series. A Fulbright scholar, lifelong senior member of the IEEE, and member of Tau Beta Pi, Sigma Nu, and Sigma Pi, he received the IEEE Outstanding Educators Award, Huntsville Section in 1990 and 1996. Dr. Poularikas holds a Ph.D from the University of Arkansas, Fayetteville, USA.

Cuprins

Vectors. Matrices. Processing of Discrete Deterministic Signals: Discrete Systems. Discrete-Time Random Processes. The Wiener Filter. Eigenvalues of Rx: Properties of the Error Surface. Newton’s and Steepest Descent Methods. The Least Mean-Square Algorithm. Variants of Least Mean-Square Algorithm. Appendices.

Descriere

This book covers the fundamentals of adaptive filtering, with a focus on the least mean square (LMS) adaptive filter. It discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions, while delivering a concise introduction to MATLAB®—complete with problems, computer experiments, and over 110 functions and script files. The text not only addresses the basics of the LMS adaptive filter algorithm but also explores the Wiener filter and its applications, details the steepest descent method, and develops the Newton’s algorithm.