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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007749
