DocumentCode
1684832
Title
Robust identification of uncertain dynamical systems where adaptation is impossible
Author
Lo, James T. ; Bassu, Devasis
Author_Institution
Dept. of Math. & Stat., Univ. of Maryland Baltimore County, MD, USA
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1558
Lastpage
1563
Abstract
This paper shows that training with the risk-averting error criterion yields a robust system identifier in the presence of an uncertain environmental parameter that is impossible to adapt to. Numerical results comparing least-squares and risk-averting identifiers illustrate the efficacy of the proposed method
Keywords
least squares approximations; optimal control; robust control; signal processing; state-space methods; least-squares method; numerical results; risk averting error criterion; risk-averting identifiers; robust control; robust identification; robust system identifier; signal processing; uncertain dynamical systems; uncertain environmental parameter; Adaptive signal processing; Error analysis; Error correction; Mathematics; Neural networks; Process control; Programmable control; Robust control; Robustness; Signal processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007749
Filename
1007749
Link To Document