A Markov Chain Monte Carlo Approach to Nonlinear Parametric System Identification

被引：4
作者：
Bai, Er-Wei [1 ,2 ]
Ishii, Hideaki [3 ]
Tempo, Roberto [4 ]
机构：
[1] Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA
[2] Queens Univ, Sch Elect, Elect Engn, Belfast BT9 6AZ, Antrim, North Ireland
[3] Tokyo Inst Technol, Dept Computat Intelligence & Syst Sci, Yokohama, Kanagawa 2268503, Japan
[4] Politecn Torino, IEIIT CNR, I-10129 Turin, Italy
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2015年 / 60卷 / 09期
基金：
美国国家科学基金会;
关键词：
Monte Carlo; parameter estimation; system identification; SET-MEMBERSHIP IDENTIFICATION; COMPLEXITY;
D O I：
10.1109/TAC.2014.2380997
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页码：2542 / 2546
页数：5
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