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% [w,x,d,y,e,p]=init_sovnlms(L1,L2,w0,x0,d0) % % Creates and initializes the variables required for the % Second Order Volterra Normalized Least Mean Squares % adaptive algorithm. %

% [w,x,d,y,e,p]=init_nlms(L,w0,x0,d0) % % Creates and initializes the variables required for the % Normalized Least Mean Squares Adaptive Filter algorithm. % % Input Parameters [Size]::

% [w,x,d,e,R,y]=init_rls(L,b,w0,x0,d0) % % Creates and initializes the variables required for the % Recursive Least Squares (RLS) Adaptive Filter. % % Input Parameters:: % L : Ada

% [W,w,x,y,e,p,yF,feF] = asptfdadjlms(NC,W,x,xn,d,yF,feF,S,SE,p,mu,b,c) % % Performs filtering and coefficient update using the % Frequency Domain Adjoint Least Mean Squares (FDADJLMS) alg

% [w,y,e,p]= asptdrnlms(x,w,d,mu,p,b,k) % % Performs filtering and coefficient update using the % Data Reusing Normalized Least Mean Squares (DRNLMS) % algorithm. DRLMS updates the filter c

% [w,y,e,p] = asptmcadjlms(w,x,e,y,s,se,d,p,mu,b) % % Performs filtering and coefficient update using the % Multichannel Adjoint Least Mean Squares (MCADJLMS) algorithm % for use in A

% [W,w,x,y,e,p,yF,fxF] = asptfdfxlms(NC,W,x,xn,d,yF,fxF,S,SE,p,mu,b,c) % % Performs filtering and coefficient update using the block processing % Frequency Domain Filtered-x Least Mean Squa

% [w,y,e]= asptdrlms(x,w,d,mu,alg,k) % % Performs filtering and coefficient update using the % Data Reusing Least Mean Squares (DRLMS) algorithm. % DRLMS updates the filter coefficients k t

% [w,y,e,p,fx] = asptmcfxlms(w,x,y,s,se,d,fx,p,mu,b) % % Performs filtering and coefficient update using the % Multichannel Filtered-X Least Mean Squares (MCFXLMS) algorithm % (also k

% [w,u,y,e,Px,Py] = asptsharf(N,M,w,u,x,d,e,c,mu,Px,Py) % % Performs filtering and coefficient update using the % Simple Hyperstable Adaptive Recursive Filter (SHARF) % algorithm. The f