Sciweavers

Share
TSP
2008

Stochastic Analysis of the LMS Algorithm for System Identification With Subspace Inputs

8 years 10 months ago
Stochastic Analysis of the LMS Algorithm for System Identification With Subspace Inputs
This paper studies the behavior of the low-rank least mean squares (LMS) adaptive algorithm for the general case in which the input transformation may not capture the exact input subspace. It is shown that the Independence Theory and the independent additive noise model are not applicable to this case. A new theoretical model for the weight mean and fluctuation behaviors is developed which incorporates the correlation between successive data vectors (as opposed to the Independence Theory model). The new theory is applied to a network echo cancellation scheme which uses partial-Haar input vector transformations. Comparison of the new model predictions with Monte Carlo simulations shows good-to-excellent agreement, certainly much better than predicted by the Independence Theory based model available in the literature.
Neil J. Bershad, José Carlos M. Bermudez, J
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TSP
Authors Neil J. Bershad, José Carlos M. Bermudez, Jean-Yves Tourneret
Comments (0)
books