Conference Proceeding

On Convergence of Proportionate-Type Nlms Adaptive Algorithms

George Washington Univ., Washington, DC
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on (impact factor: 4.63). 06/2006; DOI:10.1109/ICASSP.2006.1660601 pp.III - III In proceeding of: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, Volume: 3
Source: IEEE Xplore

ABSTRACT We specify the general form of proportionate-type NLMS adaptive algorithms and show that for sufficiently small adaptation stepsize parameter, the algorithms can be exponentially stable, globally convergent and robust to unmodeled dynamics and measurement noise. Also, we show that for small adaptation stepsize parameter and stationary inputs, behavior of proportionate-type NLMS algorithms can be modeled by proportionate-type steepest descent algorithms. This motivates designing of proportion ate-type NLMS adaptive algorithms by looking at the adjoint proportionate-type steepest descent algorithms

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Keywords

adjoint proportionate-type steepest descent algorithms
 
algorithms
 
general form
 
globally convergent
 
motivates
 
proportion ate-type NLMS adaptive algorithms
 
proportionate-type NLMS adaptive algorithms
 
proportionate-type NLMS algorithms
 
proportionate-type steepest descent algorithms
 
small adaptation stepsize parameter
 
unmodeled dynamics
 

M. Doroslovacki