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.Additional ISBNs1138417912, 1315215136, 9781138417915, 9781315215136Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB 1st Edition is written by Alexander D. Poularikas and published by CRC Press. ISBNs for Adaptive Filtering are 9781351831024, 135183102X and the print ISBNs are 9781482253351, 1482253356. Additional ISBNs include 1138417912, 1315215136, 9781138417915, 9781315215136.