Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB

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.

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.