A Novel Interacting Multiple-Model Method and Its Application to Moisture Content Prediction of ASP Flooding
作者: Shurong Li, Yulei Ge and Renlin Zang
刊名: CMES: Computer Modeling in Engineering & Sciences, 2018, Vol.114 (1), pp.95-116
来源数据库: Tech Science Press
DOI: 10.3970/cmes.2018.114.095
关键词: Interacting multiple modelRegularized extreme learning machineGaussian processMoisture content of ASP flooding.
原始语种摘要: In this paper, an interacting multiple-model (IMM) method based on data-driven identification model is proposed for the prediction of nonlinear dynamic systems. Firstly, two basic models are selected as combination components due to their proved effectiveness. One is Gaussian process (GP) model, which can provide the predictive variance of the predicted output and only has several optimizing parameters. The other is regularized extreme learning machine (RELM) model, which can improve the over-fitting problem resulted by empirical risk minimization principle and enhances the overall generalization performance. Then both of the models are updated continually using meaningful new data selected by data selection methods. Furthermore, recursive methods are employed in the two models to reduce...
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  • ASP Advanced Signal Processing
  • machine 机器
  • minimization 最小化
  • selected 被选
  • computational 计算的
  • flooding 泛滥
  • employed 就业
  • robustness 坚固性
  • match 比赛
  • meaningful 有意义的