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Adaptive Filtering
Fundamentals of Least Mean Squares with MATLAB(R)
Alexander D Poularikas
Livre broché | Anglais
135,95 €
+ 271 points
Format
Description
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.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 364
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9781482253351
- Date de parution :
- 30-09-14
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 155 mm x 226 mm
- Poids :
- 521 g
