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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: <[email protected]>

 	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: [email protected]
								Bytom, $04-Nov-2006, 16:07$
								Poland