Gaussian Process Regression for Nonlinear Time-Varying System Identification

被引：0

作者：

Bergmann, Daniel ^{[1
]}

Buchholz, Michael ^{[1
]}

Niemeyer, Jens ^{[2
]}

Remele, Joerg ^{[2
]}

Graichen, Knut ^{[1
]}

机构：

[1] Ulm Univ, Inst Measurement Control & Microtechnol, Ulm, Germany

[2] MTU Friedrichshafen GmbH, Friedrichshafen, Germany

来源：

2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a method for nonlinear system identification with Gaussian process regression. The unsupervised method is able to generate an approximation of the system with correct extrapolation behaviour, that is refined with input/output-data in the typical working area and sampled online data. Therefore, an offline model is generated, which consists of a nominal model set up by the extrapolation behaviour and a detailed model for the refinement. The method is able to keep track of time-varying systems by using the confidence information to incorporate new measurements into the online model. The performance of the proposed method is tested on different numerical examples.

引用

页码：3025 / 3031

页数：7

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