Adaptive filtering : fundamentals of least mean squares with MATLAB
著者
書誌事項
Adaptive filtering : fundamentals of least mean squares with MATLAB
CRC Press, Taylor & Francis Group : Chapman & Hall Book, c2015
- : pbk
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注記
Bibliography: p. 337
Includes index
内容説明・目次
内容説明
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.
目次
Vectors. Matrices. Processing of Discrete Deterministic Signals: Discrete Systems. Discrete-Time Random Processes. The Wiener Filter. Eigenvalues of Rx: Properties of the Error Surface. Newton's and Steepest Descent Methods. The Least Mean-Square Algorithm. Variants of Least Mean-Square Algorithm. Appendices.
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