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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®

By: Alexander D. Poularikas

eText | 19 December 2017 | Edition Number 1

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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® 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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Non-FictionMathematicsProbability & StatisticsEngineering & TechnologyEnvironmental ScienceElectronics & Communications EngineeringCommunications Engineering & TelecommunicationsEnergy Technology & EngineeringElectrical EngineeringPower Generation & Distribution

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Published: 30th September 2014

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