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Adaptive Filtering

version 2.0 (197 KB) by Paulo S. R. Diniz
MATLAB files to implement all Adaptive Filtering Algorithms in this book.

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Updated 18 Feb 2020

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MATLAB files to implement all Adaptive Filtering Algorithms in the book by Paulo S. R. Diniz, Adaptive Filtering Algorithms and Practical Implementation, Fifth Edition, Springer, New York, 2020. The same toolbox applies to the fourth edition of the book.
MATLAB files by Guilherme O. Pinto, Markus V. S. Lima, Wallace A. Martins, Luiz W. P. Biscainho, and Paulo S. R. Diniz.
This book presents a concise overview of adaptive filtering, covering as many as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduate or first-year graduate courses in adaptive signal processing and adaptive filters.
The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference.
In the fifth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.
The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes:

- Many analytical and simulation examples.
- A comprehensive chapter on Kalman filters, including ensemble Kalman filtering.
- Updated problems and references

Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more.

Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner.

An instructor`s manual, a set of slides, and MATLAB codes for all of the algorithms described in the text are also available. Useful to both professional researchers and students, the text includes hundreds of problems, numerous examples, and over 150 illustrations. It is of primary interest to those working in signal processing, communications, and circuits and systems.

It will also be of interest to those working in power systems, networks, learning systems, machine learning, and intelligent systems.

For book ordering information, please visit: http://www.mathworks.com/support/books/book48941.html

Cite As

Paulo S. R. Diniz (2021). Adaptive Filtering (https://www.mathworks.com/matlabcentral/fileexchange/3582-adaptive-filtering), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
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Adaptive_filtering_toolbox_v4/Blind_Adaptive_Filtering

Adaptive_filtering_toolbox_v4/Fast_Transversal_RLS_Algorithms

Adaptive_filtering_toolbox_v4/IIR_Adaptive_Filters

Adaptive_filtering_toolbox_v4/LMS-based_Algorithms

Adaptive_filtering_toolbox_v4/Lattice-based_RLS_Algorithms

Adaptive_filtering_toolbox_v4/Nonlinear_Adaptive_Filters

Adaptive_filtering_toolbox_v4/QR-decomposition-based_RLS_Algorithms

Adaptive_filtering_toolbox_v4/RLS_Algorithms

Adaptive_filtering_toolbox_v4/Set-membership_Algorithms

Adaptive_filtering_toolbox_v4/Subband_Adaptive_Filters

Adaptive_filtering_toolbox_v4/Utilities