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Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB®

Alexander D. Poularikas, "Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB®"
2014 | pages: 361 | ISBN: 1482253356 | PDF | 21,0 mb

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.

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Tags: Adaptive, Filtering, Fundamentals, Squares, MATLAB

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