Adaptive Filtering Fundamentals of Least Mean Squares with MATLAB®
Auteur : Poularikas Alexander D.
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.
Date de parution : 10-2014
15.6x23.4 cm
Date de parution : 06-2017
15.6x23.4 cm
Thème d’Adaptive Filtering :
Mots-clés :
Book MATLAB; Book MATLAB Program; Signals and Systems; Book MATLAB Function; Systems; Minimum MSE; Signals; Filter Coefficient Vector; Random Digital Signal Processing; MATLAB Function; Signal Processing; LMS Algorithm; Filter Coefficient; Adaptive Filtering; Step-size Parameter; LMS adaptive filter; Discrete Random Signals; Least mean-square; MATLAB Command Window; Adaptive Filter; MSE Surface; Steepest Descent Algorithm; Steepest Descent Method; Adaptive Filter Coefficients; Fir System; Finite Geometric Series; Adaptive LMS Filter; Wiener Filter; DTFT; Stochastic Gradient Algorithms; Hamming Window; Time Varying Step Size; Linearly Independent