Adaptive Filtering Fundamentals of Least Mean Squares with MATLAB® |
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| Author:
| Poularikas, Alexander D. |
| ISBN: | 978-1-351-82233-6 |
| Publication Date: | Dec 2017 |
| Publisher: | Taylor & Francis Group
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| Imprint: | CRC Press |
| Book Format: | Digital (delivered electronically) |
| List Price: | USD $89.95USD $325.00USD $240.00 |
Book Description:
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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:
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.