A variational Bayesian approach for robust identification of linear parameter varying systems using mixture laplace distributions
作者: Xinpeng LiuXianqiang Yang
作者单位: 1Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
刊名: Neurocomputing, 2020, Vol.395 , pp.15-23
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2020.01.088
关键词: Linear parameter-varying systemsRobust identificationLaplace distributionVariational inference
原始语种摘要: Abstract(#br)The robust identification problem of the linear parameter varying (LPV) systems with output data corrupted by outliers is considered in this paper. The local identification approach is used, and the LPV model is obtained by interpolating the local models with an exponential weighting function. In order to handle outliers that could occur in industrial processes, the corresponding probabilistic model is established with the process noise assumed to be mixture Laplace distributed, then the formulas to iteratively update the unknown model parameters and noise-free output are derived under the Variational Bayesian (VB) framework, which approximates the required posteriors and could avoid high dimensional integrals. One numerical example and a practical chemical process are...
全文获取路径: Elsevier  (合作)
影响因子:1.634 (2012)

  • varying 变化的
  • variational 变化的
  • robust 牢固的
  • parameter 参数
  • interpolating 内插
  • identification 辨认
  • probabilistic 概率的
  • distributed 分布的
  • exponential 指数的
  • mixture 混合物