Contraction mapping: an important property in adaptive filters

Author

Rupp, Markus

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA

fYear

1994

fDate

2-5 Oct 1994

Firstpage

273

Lastpage

276

Abstract

Shows that many adaptive filters used for system identification are contraction mappings. Applying deterministic methods the authors give conditions under which algorithms, like least mean square, normalized least mean square, modified least mean square with delayed update, modified filtered-X least mean square, affine projection, and recursive least square are a contraction mapping contracting. Based on this result, the authors investigate the algorithms´ convergence rate for initialization phase and tracking

Keywords

adaptive filters; least mean squares methods; adaptive filters; affine projection; contraction mappings; convergence rate; delayed update; deterministic methods; initialization phase; least mean square; modified filtered-X least mean square; modified least mean square; normalized least mean square; recursive least square; system identification; tracking; Adaptive filters; Additive noise; Convergence; Equations; Error correction; Finite impulse response filter; Least squares approximation; Least squares methods; System identification; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop, 1994., 1994 Sixth IEEE

Conference_Location

Yosemite National Park, CA

Print_ISBN

0-7803-1948-6

Type

conf

DOI

10.1109/DSP.1994.379824

Filename

379824