Necessary and Sufficient Conditions for Stability of LMS
1995 (English)Report (Other academic)
Guo and Ljung (1995) established some general results on exponential stability of random linear equations, which can be applied directly to the performance analysis of a wide class of adaptive algorithms, including the basic LMS ones, without requiring stationarity, independency, and boundedness assumptions of the system signals. The current paper attempts to give a complete characterization of the exponential stability of the LMS algorithms by providing a necessary and sufficient condition for such a stability in the case of possibly unbounded, nonstationary, and non-φ-mixing signals. The results of this paper can be applied to a very large class of signals, including those generated from, e.g., a Gaussian process via a time-varying linear filter. As an application, several novel and extended results on convergence and the tracking performance of LMS are derived under various assumptions. Neither stationarity nor Markov-chain assumptions are necessarily required in the paper.
Place, publisher, year, edition, pages
1995. , 25 p.
LiTH-ISY-R, ISSN 1400-3902 ; 1805
Least mean squares algorithm, LMS
IdentifiersURN: urn:nbn:se:liu:diva-55289ISRN: LiTH-ISY-R-1805OAI: oai:DiVA.org:liu-55289DiVA: diva2:315836