DocumentCode
325397
Title
Identification of the smallest unfalsified model set based on stochastic noisy data
Author
Fukushima, Hiroaki ; Sugie, Toshiharu
Author_Institution
Dept. of Appl. Syst. Sci., Kyoto Univ., Japan
Volume
5
fYear
1998
fDate
21-26 Jun 1998
Firstpage
3204
Abstract
We propose a new model set identification method using experimental data contaminated by stochastic noise. We find the smallest model set which is consistent with the experimental data by separating the output error into the deterministic part due to the unmodeled dynamics and the stochastic noise part. Furthermore, the effectiveness of this method is shown by numerical examples
Keywords
dynamics; identification; modelling; noise; stochastic processes; model set identification method; output error; smallest unfalsified model set; stochastic noisy data; unmodeled dynamics; Additive noise; Artificial intelligence; Digital TV; Gaussian distribution; Integrated circuit noise; Stochastic processes; Stochastic resonance; Stochastic systems; Transfer functions; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 1998. Proceedings of the 1998
Conference_Location
Philadelphia, PA
ISSN
0743-1619
Print_ISBN
0-7803-4530-4
Type
conf
DOI
10.1109/ACC.1998.688453
Filename
688453
Link To Document