A systolic algorithm for adaptive set membership identification

Author

Odeh, S. ; Deller, J., Jr.

Author_Institution

Dept. of Electr. Eng., Michigan State Univ., E. Lansing, MI, USA

fYear

1990

fDate

3-6 Apr 1990

Firstpage

2419

Abstract

An adaptive set membership identification algorithm with a very flexible forgetting scheme is presented. In preliminary experiments, the method yields highly accurate estimates using very few of the data, and quickly adapts to fast-changing dynamics. A compact systolic architecture to implement this algorithm is developed which uses O (m) cells and reduces the computational complexity to O(m) operations per observation, where m represents the number of parameters to be estimated in a linear system or signal model

Keywords

computational complexity; least squares approximations; parallel algorithms; parallel architectures; parameter estimation; signal processing; systolic arrays; adaptive set membership identification; compact systolic architecture; computational complexity; flexible forgetting scheme; linear system model; parameter estimation; signal model; systolic algorithm; weighted recursive least squares algorithm; Adaptive signal processing; Computational complexity; Computer architecture; Control systems; Laboratories; Least squares methods; Linear systems; Parameter estimation; Programmable control; Samarium; Signal processing algorithms; Speech processing; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.116075

Filename

116075