Robust identification approach for nonlinear state-space models

被引：10
|
作者：
Liu, Xin [1 ]
Yang, Xianqiang [1 ,2 ]
机构：
[1] Harbin Inst Technol, Sch Astronaut, Harbin 150080, Heilongjiang, Peoples R China
[2] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150080, Heilongjiang, Peoples R China
来源：
NEUROCOMPUTING | 2019年 / 333卷
基金：
中国国家自然科学基金;
关键词：
Nonlinear system identification; Robustness; Student's t-distribution; Particle smoother; Expectation-maximization algorithm; SYSTEM-IDENTIFICATION; MULTIMODEL APPROACH; LPV APPROACH;
D O I：
10.1016/j.neucom.2018.12.017
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The identification of nonlinear state-space model (NSSM) with output observations corrupted by outliers is investigated in this paper. The outlier is commonly encountered in practical industrial processes which should not be ignored in nonlinear processes modeling. The statistical scheme based on the Student's t-distribution is applied to resist the outlier and the expectation-maximization (EM) algorithm is employed to simultaneously identify the undetermined model and noise parameters. A particle smoother is introduced and used to approximately calculate the desired Q-function. The usefulness of the proposed approach is demonstrated via the numerical and mechanical examples. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：329 / 338
页数：10
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