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.

Book Details

• Author: Alexander D. Poularikas
• Publisher: CRC Press
• eBook ISBN: 9781351831024, 135183102X
• Print ISBN: 9781482253351, 1482253356
• Other ISBNs: 1138417912, 1315215136, 9781138417915, 9781315215136