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.
引用
收藏
页码：2542 / 2546
页数：5
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