Shurong Li1,*, Yulei Ge2, Renlin Zang2
CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.1, pp. 95-116, 2018, DOI:10.3970/cmes.2018.114.095
Abstract 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… More >
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