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
收藏
页码：3025 / 3031
页数：7
相关论文
共 50 条
  • [1] Nonlinear Time-Varying System Identification with Recursive Gaussian Process
    Seo, Kwangwoo
    Yamakita, Masaki
    2017 AMERICAN CONTROL CONFERENCE (ACC), 2017, : 825 - 830
  • [2] Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression
    Hallemans, N.
    Lataire, J.
    Pintelon, R.
    IFAC PAPERSONLINE, 2020, 53 (02): : 1001 - 1006
  • [3] Design of Model Predictive Control for Time-Varying Nonlinear System Based on Gaussian Process Regression Modeling
    Zhou, Min
    Guo, Zhao-Qin
    Li, Xiang
    2016 IEEE 21ST INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2016,
  • [4] Gaussian process regression for modeling of time-varying systems
    Bergmann, Daniel
    Graichen, Knut
    AT-AUTOMATISIERUNGSTECHNIK, 2019, 67 (08) : 637 - 647
  • [5] Online Gaussian Process Regression for Time-Varying Manufacturing Systems
    Hu, Jinwen
    Li, Xiang
    Ou, Yanjing
    2014 13TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION (ICARCV), 2014, : 1118 - 1123
  • [6] Nonlinear time-varying system identification based on time-varying NARMA model
    Pang, Shi-Wei
    Yu, Kai-Ping
    Zou, Jing-Xiang
    Gongcheng Lixue/Engineering Mechanics, 2006, 23 (12): : 25 - 29
  • [7] Machine Learning With Gaussian Process Regression For Time-Varying Channel Estimation
    Simeon, Richard
    Kim, Taejoon
    Perrins, Erik
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 3400 - 3405
  • [8] The Identification of Stochastic Time-varying Parameters for Nonlinear System
    Xie Guo
    Zhang Dan
    Qian Fucai
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 2137 - 2141
  • [9] Output-only recursive identification of time-varying structures using a Gaussian process regression TARMA approach
    Ma, Z. -S.
    Liu, L.
    Zhou, S. -D.
    Naets, F.
    Heylen, W.
    Desmet, W.
    PROCEEDINGS OF ISMA2016 INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING AND USD2016 INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS, 2016, : 2859 - 2871
  • [10] Time-Varying Gaussian Process Bandit Optimization
    Bogunovic, Ilija
    Scarlett, Jonathan
    Cevher, Volkan
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 51, 2016, 51 : 314 - 323
12345