Title :
Contraction mapping: an important property in adaptive filters
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA
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;
Conference_Titel :
Digital Signal Processing Workshop, 1994., 1994 Sixth IEEE
Conference_Location :
Yosemite National Park, CA
Print_ISBN :
0-7803-1948-6
DOI :
10.1109/DSP.1994.379824
