Recursive System Identification Using Outlier-Robust Local Models

被引：1

作者：

Bessa, Jessyca A. ^{[1
]}

Barreto, Guilherme A. ^{[1
]}

机构：

[1] Univ Fed Ceara, Grad Program Teleinformat Engn, Campus Pici, Fortaleza, Ceara, Brazil

来源：

IFAC PAPERSONLINE | 2019年 / 52卷 / 01期

关键词：

System identification; neural networks; local linear models; outliers; M-estimation; NETWORKS;

D O I：

10.1016/j.ifacol.2019.06.101

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we revisit the design of neural-network based local linear models for dynamic system identification aiming at extending their use to scenarios contaminated with outliers. To this purpose, we modify well-known local linear models by replacing their original recursive rules with outlier-robust variants developed from the M-estimation framework. The performances of the proposed variants are evaluated in free simulation tasks over 3 benchmarking datasets. The obtained results corroborate the considerable improvement in the performance of the proposed models in the presence of outliers. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：436 / 441

页数：6

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