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README
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ADAPTIVE FILTER ALGORITHMS PACKAGE FOR MATLAB ver. 1.0
This package was created during completing „Combined Noise and Echo Reduction”
M. Sc. Thesis (Silesian University of Technology, Gliwice, Poland) for simulation purposes (full thesis is available (in Polish) in cner.pdf file).
The package covers main topics in adaptive algorithms. List of included procedures is as follows:
Stochastic gradient algorithms
APAOCF Affine Projection Algorithm with Orthogonal Correction Factors
DCTLMS Discrete Cosine Transform Least Mean Square
DFTLMS Discrete Fourier Transform Least Mean Square
DHTLMS Discrete Hartley Transform Least Mean Square
eAPA e-Affine Projection Algorithms
eNLMS e-Normalized Least Mean Square
epNLMS e-Power Normalized Least Mean Square
ePRA e-Partial Rank Algorithm
LeakyLMS Leaky Least Mean Square
LMF Least Mean Fourth
LMMN Least Mean Mixed Norm
LMS Least Mean Square
seLMS Signed Error Least Mean Square
srLMS Signed Regressor Least Mean Square
ssLMS Sign-Sign Least Mean Square
Block algorithms
Bconv Efficient block convolution via DFT
BDCTconv Efficient block convolution via DCT
BDHTconv Efficient block convolution via DHT
cDCTBpNLMS Constrained DCT Block Power Normalized Least Mean Square
cDFTBpNLMS Constrained DFT Block Power Normalized Least Mean Square
cDHTBpNLMS Constrained DHT Block Power Normalized Least Mean Square
dlBconv Delayless efficient block convolution via DFT
fcDCTBpNLMS Constrained DCT Block Power Normalized Least Mean Square with error estimate by convolution in fullband
fcDFTBpNLMS Constrained DFT Block Power Normalized Least Mean Square with error estimate by convolution in fullband
fcDHTBpNLMS Constrained DHT Block Power Normalized Least Mean Square with error estimate by convolution in fullband
efcDFTBpNLMS Constrained DFT Block Power Normalized Least Mean Square with error estimate by efficient block convolution in fullband
dlefcDFTBpNLMS Delayless constrained DFT Block Power Normalized Least Mean Square with error estimate by efficient block convolution in fullband
uDCTBpNLMS Unconstrained DCT Block Power Normalized Least Mean Square
uDFTBpNLMS Unconstrained DFT Block Power Normalized Least Mean Square
uDHTBpNLMS Unconstrained DHT Block Power Normalized Least Mean Square
Infinite memory Recursive Least Squares
FAEST Fast a Posteriori Error Sequential Technique
FARLS Fast Array Recursive Least Squares
FARLSL Fast Array Recursive Least Squares Lattice based on QR Decomposition
FKF Fast Kalman Filter
FTF Fast Transversal Filters
QRRLS QR Recursive Least Squares
IQRRLS Inverse QR Recursive Least Squares
NRLSL Normalized Recursive Least Squares Lattice
RLS Recursive Least Squares
SFTF Stabilized Fast Transversal Filters
RLSLpos A Posteriori Based Recursive Least Squares Lattice
RLSLposf A Posteriori Based Error Feedback Recursive Least Squares Lattice
RLSLpri A Priori Based Recursive Least Squares Lattice
RLSLprif A Priori Based Error Feedback Recursive Least Squares Lattice
Finite memory Recursive Least Squares
SWRLS Sliding Window Recursive Least Squares
SWIQRRLS Sliding Window Inverse QR Recursive Least Squares
SWQRRLS Sliding Window QR Recursive Least Squares
SWFARLS Sliding Window Fast Array Recursive Least Squares
SWFARLSL Sliding Window Fast Array Recursive Least Squares Lattice Based on QR Decomposition
SWRLSLpos Sliding Window a Posteriori Based Recursive Least Squares Lattice
RLSLposf Sliding Window a Posteriori Based Error Feedback Recursive Least Squares Lattice
SWRLSLpri Sliding Window A Priori Based Recursive Least Squares Lattice
SWRLSLprif Sliding Window a Priori Based Error Feedback Recursive Least Squares Lattice
Miscellaneous algorithms
BEFAP_FARLS Block Exact Fast Affine Projection Algorithm with Fast Array Recursive Least Squares prediction
BEFAP_FQRD Block Exact Fast Affine Projection Algorithm with Fast Array Recursive Least Squares Lattice prediction
For absolutely outstanding reference in adaptive filtering look „Fundamentals of Adaptive Filtering”
by Ali H. Sayed. Also check „Adaptive Filters. Theory and Applications” by B. Farhang-Boroujeny.
LICENSE:
Adaptive Filter Algorithms Package for Matlab ver. 1.0
Copyright (C) <2006> Bartosz Zator
Have you found a bug? Please let me know: <braton@gmail.com>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Bartosz Zator M. Sc.
E-mail: braton@gmail.com
Bytom, $04-Nov-2006, 16:07$
Poland