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

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

% [w,y,e,p]= asptnlms(x,w,d,mu,p,b) % % Performs filtering and coefficient update using the % Normalized Least Mean Squares Adaptive algorithm. % % Input Parameters [Size]:: % x : in

% [w,y,e,p]= asptrdrnlms(x,w,d,mu,p,b,k) % % Performs filtering and coefficient update using the % Recent Data Reusing Normalized Least Mean Squares % (RDRNLMS) algorithm. % RDRNLMS upda

% [w,y,e,p]= asptleakynlms(x,w,d,mu,a,p,b) % % Performs filtering and coefficient update using the % Leaky Normalized Least Mean Squares algorithm. The % update equation is given by %

% [w,x,y,e,p]=asptbnlms(x,xn,dn,w,mu,p,b) % % Performs filtering and coefficient update using the % Block Normalized Least Mean Squares (BNLMS) algorithm. % BNLMS updates the N filter c

% [w,x,y,e,d,p,fx] = init_mcfxlms(L,Nref,Nact,Nsens,s,se,w0,x0,d0,y0,fx0) % % Creates and initializes the variables required for the % Multichannel Filtered-X Least Mean Squares (MCFXLMS)

% [w,x,y,d,e,p] = init_mcadjlms(L,Nref,Nact,Nsens,s,se,w0,x0,d0,y0,e0) % % Creates and initializes the variables required for the % Multi-channel Adjoint LMS (MCADJLMS) algorithm % for

% [w,y,e,xb]= asptsovlms(xn,xb,w,d,mu,L1,L2,alg) % % Sample per sample filtering and coefficient update using the % Second Order Volterra Least Mean Squares or one of its variants. % The LM

% [W,X,x,y,e,Px,w]=asptpbfdaf(M,x,xn,dn,X,W,mu,n,c,b,Px) % % Performs filtering and coefficient update using the % Partitioned Block Frequency Domain Adaptive algorithm. % % Input Paramete