Adaptive Filtering

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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.