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Author Voltz, P. ♦ Kozin, F.
Sponsorship IEEE Control Systems Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1963
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Physics ♦ Electricity & electronics
Subject Keyword Convergence ♦ Adaptive algorithm ♦ Least squares approximation ♦ Sufficient conditions ♦ Least mean square algorithms ♦ Adaptive estimation ♦ System identification ♦ Adaptive control ♦ Stochastic processes
Abstract A convergence proof is discussed for the normalized least-mean-square (LMS) algorithm for ergodic inputs. The approach is based on interpreting the algorithm as a sequence of relaxed projection operators by which the key contraction property is derived. The proof technique is strongly motivated by physical intuition, and provides additional insight into LMS-type algorithms under ergodic inputs. Embedded in the development is a slight generalization to a random time-varying gain parameter. This allows the incorporation of variations such as the LMS and signed LMS algorithms.<<ETX>>
Description Author affiliation :: Polytech. Univ., Farmingdale, NY, USA
ISSN 00189286
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1989-03-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 34
Issue Number 3
Size (in Bytes) 230.03 kB
Page Count 3
Starting Page 325
Ending Page 327


Source: IEEE Xplore Digital Library