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% [W,y,e,p,w] = aspttdftaf(x,W,d,mu,p,b,T) % % Performs filtering and coefficient update using the % Transform Domain Fault Tolerant Adaptive Filter (TDFTAF). % TDFTAF contains R redunda

% [W,w,x,y,e,p,yF,feF] = asptmcfdadjlms(NC,W,x,xn,dn,yF,feF,S,SE,p,mu,b,c) % % Performs filtering and coefficient update using the % Multi Channel Frequency Domain Adjoint LMS (MCFDADJLMS)

% [w,y,e,R]=asptrls(x,w,d,R,a) % % Performs filtering and coefficient update using the % Recursive Least Squares (RLS) Adaptive algorithm. % % Input Parameters [Size]:: % x : vector

% [w,x,d,y,e,g,mu] = init_mvsslms(L,w0,x0,d0,mu0,g0) % Creates and initializes the variables required for the % Variable Step Size LMS algorithm. % % Input Parameters:: % L : adapt

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

% [w,x,y,d,e,p] = init_adjlms(L,s,se,w0,x0,d0,y0,e0) % % Creates and initializes the variables required for the % Adjoint Least Mean Squares (ADJLMS) Adaptive Filter algorithm % for us

% [w,y,e,xb,p]= asptsovnlms(xn,xb,w,d,mu,L1,L2,p,b) % % Performs filtering and coefficient update using the % Second Order Volterra Normalized Least Mean Squares % Adaptive Filter algori

% [w,x,d,y,e,g,mu]=init_sovvsslms(L1,L2,w0,x0,d0,mu0,g0) % % Creates and initializes the variables required for the % Second Order Volterra Variable Step Size Least Mean % Squares ad

% [w,x,y,d,e,p,fx] = init_fxlms(L,s,se,w0,x0,d0,y0) % % Creates and initializes the variables required for the % Filtered-x Least Mean Squares (FXLMS) Adaptive Filter algorithm % for u

% [w,y,e]= asptlclms(x,w,d,mu,c,a) % % Performs filtering and coefficient update using the % Linearly Constrained LMS adaptive algorithm, % subject to the constraint c' * w = a. % % In