DocumentCode
2030112
Title
An interpretable and converging set-membership algorithm
Author
Nayeri, M. ; Liu, M.S. ; Deller, J.R.
Author_Institution
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA
Volume
4
fYear
1993
fDate
27-30 April 1993
Firstpage
472
Abstract
Set membership (SM)-based techniques, with least square error overlay, suffer from a trade-off between interpretability and proof of convergence. The authors introduce a modified SM algorithm with ´forgetting´ covariance updating in conjunction with minimum volume data selecting strategy. The convergence properties of this algorithm and its resemblance to the stochastic approximation method are discussed.<>
Keywords
convergence; least squares approximations; set theory; variational techniques; covariance updating; interpretability; least square error overlay; minimum volume data selecting strategy; proof of convergence; set-membership algorithm; stochastic approximation method;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location
Minneapolis, MN, USA
ISSN
1520-6149
Print_ISBN
0-7803-7402-9
Type
conf
DOI
10.1109/ICASSP.1993.319697
Filename
319697
Link To Document