Alexander D. Poularikas

Adaptive Filtering (eBook, PDF)

Fundamentals of Least Mean Squares with MATLAB®

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 363
• Erscheinungstermin: 19. Dezember 2017
• Englisch
• ISBN-13: 9781482253368
• Artikelnr.: 50533490

Autorenporträt

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.

Inhaltsangabe

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.