A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification

Er-Wei Bai; Hideaki Ishii; Roberto Tempo

doi:10.1109/TAC.2014.2380997

Back

A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification

Journal article

Peer reviewed

A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification

Er-Wei Bai, Hideaki Ishii and Roberto Tempo

IEEE transactions on automatic control, Vol.60(9), pp.2542-2546

09/2015

DOI: 10.1109/TAC.2014.2380997

View Online

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.

Approximation methods

Noise

Computational complexity

Ellipsoids

Convergence

Random sequences

Details

Title: Subtitle: A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification
Creators: Er-Wei Bai - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Hideaki Ishii - Dept. of Comput. Intell. & Syst. Sci., Tokyo Inst. of Technol., Yokohama, Japan
Roberto Tempo - IEIIT, Politec. di Torino, Turin, Italy
Resource Type: Journal article
Publication Details: IEEE transactions on automatic control, Vol.60(9), pp.2542-2546
DOI: 10.1109/TAC.2014.2380997
ISSN: 0018-9286
eISSN: 1558-2523
Publisher: IEEE
Grant note: NSF CNS-1329057 257462 HYCON2 / European Union Seventh Framework Programme [FP7/2007-2013]
Language: English
Date published: 09/2015
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984083208002771

Metrics

22 Record Views

5 Times Cited - Web of Science