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

    Posted By: step778
    Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® (Repost)

    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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