Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB(R)

by Alexander D. Poularikas and Alexander D Poularikas

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.

• Format: Paperback
• Language: English
• ISBN: 1482253356
• ISBN13: 9781482253351
• Release Date: September 2014
• Publisher: CRC Press
• Length: 364 Pages
• Weight: 1.15 lbs.
• Dimensions: 0.8" x 6.1" x 8.9"