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(‎R‎)‎ 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(‎R‎)‎‎,‎ 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(‎R‎)‎ clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real‎-world problems‎.‎

• Author‎:‎ Alexander D‎.‎ Poularikas
• Publisher‎:‎ CRC Press
• ISBN‎:‎ 9781482253351