Facing undermodelling in Sign-Perturbed-Sums system identification

被引：8

作者：

Care, A. ^{[1
]}

Campi, M. C. ^{[1
]}

Csaji, B. Cs ^{[2
]}

Weyer, E. ^{[3
]}

机构：

[1] Univ Brescia, Brescia, Italy

[2] SZTAKI Inst Comp Sci & Control, Budapest, Hungary

[3] Univ Melbourne, Melbourne, Vic, Australia

来源：

SYSTEMS & CONTROL LETTERS | 2021年 / 153卷

关键词：

System identification; Confidence regions; Finite sample results; Least squares; Distribution-free results; Uncertainty evaluation; NONASYMPTOTIC CONFIDENCE-REGIONS; KERNEL METHODS; MODEL; PARAMETERS; LAGUERRE; SET;

D O I：

10.1016/j.sysconle.2021.104936

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Sign-Perturbed Sums (SPS) is a finite sample system identification method that constructs exact, non-asymptotic confidence regions for the unknown parameters of linear systems without using any knowledge about the disturbances except that they are symmetrically distributed. In the available literature, the theoretical properties of SPS have been investigated under the assumption that the order of the system model is known to the user. In this paper, we analyse the behaviour of SPS when the model assumed by the user does not match the data generation mechanism, and we propose a new SPS algorithm able to detect the circumstance that the model order is incorrect. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：10

共 40 条

[1] Randomized Control Strategies Under Arbitrary External Noise [J].