In the areas of acoustic research or applications that deal with not-precisely-known or variable conditions, a method of adaptation to the uncertainness or changes is usually necessary. When searching for an adaptation algorithm, it is hard to overlook the least mean squares (LMS) algorithm. Its simplicity, speed of computation, and robustness has won it a wide area of applications: from telecommunication, through acoustics and vibration, to seismology. The algorithm, however, still lacks a full theoretical analysis. This is probabely the cause of its main drawback: the need of a careful choice of the step size - which is the reason why so many variable step size flavors of the LMS algorithm has been developed.

This paper contributes to both the above mentioned characteristics of the LMS algorithm. First, it shows a derivation of a new necessary condition for the LMS algorithm convergence. The condition, although weak, proved useful in developing a new variable step size LMS algorithm which appeared to be quite different from the algorithms known from the literature. Moreover, the algorithm proved to be effective in both simulations and laboratory experiments, covering two possible applications: adaptive line enhancement and active noise control.

JO - Archives of Acoustics L1 - http://journals.pan.pl/Content/101569/PDF/05_paper.pdf L2 - http://journals.pan.pl/Content/101569 PY - 2012 IS - No 1 EP - 40 KW - signal processing KW - adaptive algorithms KW - least mean squares KW - active noise control KW - system identification A1 - Bismor, Dariusz PB - Committee on Acoustics PAS, PAS Institute of Fundamental Technological Research, Polish Acoustical Society VL - vol. 37 JF - Archives of Acoustics DA - 2012 T1 - LMS Algorithm Step Size Adjustment for Fast Convergence SP - 31 UR - http://journals.pan.pl/dlibra/docmetadata?id=101569 DOI - 10.2478/v10168-012-0005-8 ER -