Identification of state-dependent parameter models with support vector regression

被引：17
|
作者：
Toivonen, H. T. [1 ]
Totterman, S. [1 ]
Akesson, B. [1 ]
机构：
[1] Abo Akad Univ, Fac Technol, FIN-20500 Turku, Finland
来源：
INTERNATIONAL JOURNAL OF CONTROL | 2007年 / 80卷 / 09期
关键词：
D O I：
10.1080/00207170701378673
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A support vector regression approach is presented for the identification of state-dependent parameter ARX models, whose parameters are described as functions of past inputs and outputs. The problem of identifying the state-dependent parameters reduces to a standard support vector regression problem with a kernel function which is defined in terms of the kernels used to represent the individual parameters. Numerical examples show that the support vector method gives accurate parameter estimates for systems which have a state-dependent parameter representation.
引用
收藏
页码：1454 / 1470
页数：17
相关论文
共 50 条
  • [31] On-line nonparametric regression to learn state-dependent disturbances
    de Kruif, BJ
    de Vries, TJA
    PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL, 2003, : 75 - 80
  • [32] PARAMETER ESTIMATION BY QUASILINEARIZATION IN DIFFERENTIAL EQUATIONS WITH STATE-DEPENDENT DELAYS
    Hartung, Ferenc
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, 2013, 18 (06): : 1611 - 1631
  • [33] Parameter estimation for Weibull distribution using support vector regression
    Ling, Dan
    Huang, Hong-Zhong
    Miao, Qiang
    Yang, Bo
    DETC2007: PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNOLOGY CONFERENCE AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, VOL 4, 2008, : 445 - 449
  • [34] A Parameter Selection Method for Support Vector Interval Regression Model
    Chen, Yongqi
    MECHANICAL, MATERIALS AND MANUFACTURING ENGINEERING, PTS 1-3, 2011, 66-68 : 626 - 630
  • [35] Support vector regression performance analysis and systematic parameter selection
    Lin, PT
    Su, SF
    Lee, TT
    Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, 2005, : 877 - 882
  • [36] Material parameter inverse technique based on support vector regression
    School of Traffic and Logistics, Central South University of Forestry and Technology Changsha, Changsha 410004, China
    不详
    Jixie Gongcheng Xuebao, 1600, 6 (90-95):
  • [37] Quantitative Assessment of State-Dependent Atmospheric Motion Vector Uncertainties
    Posselt, Derek J.
    Wu, Longtao
    Mueller, Kevin
    Huang, Lei
    Irion, Fredrick W.
    Brown, Shannon
    Su, Hui
    Santek, David
    Velden, Christopher S.
    JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, 2019, 58 (11) : 2479 - 2495
  • [38] State-dependent parameter modelling and identification of stochastic non-linear sampled-data systems
    Akesson, Bernt M.
    Toivonen, Hannu T.
    JOURNAL OF PROCESS CONTROL, 2006, 16 (08) : 877 - 886
  • [39] Nonlinear state-dependent impulsive system and its parameter identification in microbial fed-batch culture
    Shen, Bangyu
    Zhai, Jingang
    Gao, Jinggui
    Feng, Enmin
    Xiu, Zhilong
    APPLIED MATHEMATICAL MODELLING, 2016, 40 (02) : 1126 - 1136
  • [40] Intelligent Identification of Boiling Water Reactor State Utilizing Relevance Vector Regression Models
    Alamaniotis, Miltiadis
    Cappelli, Mauro
    JOURNAL OF NUCLEAR ENGINEERING AND RADIATION SCIENCE, 2018, 4 (02):
12345