DocumentCode
743432
Title
A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification
Author
Bai, Er-Wei ; Ishii, Hideaki ; Tempo, Roberto
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Volume
60
Issue
9
fYear
2015
Firstpage
2542
Lastpage
2546
Abstract
Nonlinear system identification is discussed in a mixed set-membership and statistical setting. A Markov chain Monte Carlo (MCMC) approach is proposed that estimates the feasible parameter set, the minimum volume outer-bounding ellipsoid and the minimum variance estimate. The proposed algorithm is proved to be convergent and enjoys some desirable properties. Further, its computational complexity and numerical accuracy are studied.
Keywords
Markov processes; Monte Carlo methods; computational complexity; convergence; nonlinear systems; parameter estimation; Markov chain Monte Carlo approach; computational complexity; convergence; minimum variance estimate; mixed set-membership; nonlinear parametric system identification; outer-bounding ellipsoid; parameter set estimation; Approximation methods; Computational complexity; Convergence; Ellipsoids; Noise; Random sequences; Monte Carlo; parameter estimation; system identification;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.2014.2380997
Filename
6985540
Link To Document