Exponential convergence of adaptive algorithms

Author

Moustakides, George V.

Author_Institution

Dept. of Comput. Eng. & Inf., Patras Univ., Greece

fYear

1998

fDate

16-21 Aug 1998

Firstpage

262

Abstract

We introduce a novel method for analyzing a well known class of adaptive algorithms. By combining developments from the theory of Markov processes and long existing results from the theory of perturbations of linear operators we study first the behavior and convergence properties of a class of products of random matrices. This in turn allows for the analysis of the first and second order statistics of the adaptive algorithms yielding estimates for the exponential rate of convergence and the covariance matrix of the estimation error

Keywords

Markov processes; adaptive signal processing; convergence; covariance matrices; mathematical operators; statistical analysis; Markov processes; adaptive algorithms; covariance matrix; estimation error; exponential convergence rate; first order statistics; perturbations of linear operators; random matrices; second order statistics; Adaptive algorithm; Algorithm design and analysis; Convergence; Covariance matrix; Eigenvalues and eigenfunctions; Estimation error; Informatics; Markov processes; Recursive estimation; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1998. Proceedings. 1998 IEEE International Symposium on

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-5000-6

Type

conf

DOI

10.1109/ISIT.1998.708867

Filename

708867