The Ordinary Kriging in Multivariate Dynamic Modelling and Multistep-Ahead Prediction

被引：8

作者：

Shokry, Ahmed ^{[1
,2
]}

Espuna, Antonio ^{[1
]}

机构：

[1] Univ Politecn Cataluna, Dept Chem Engn, EEBE Eduard Maristany 14, Barcelona 08019, Spain

[2] Zagazig Univ, Fac Engn, Dept Mech Design & Prod Engn, Zagazig, Egypt

来源：

28TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING | 2018年 / 43卷

关键词：

Multivariate dynamic modelling; Multistep-ahead prediction; Complex Nonlinear processes; Surrogate Models; Ordinary Kriging; ANNs; Gaussian models;

D O I：

10.1016/B978-0-444-64235-6.50047-4

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiStep-Ahead Prediction (MSAP) of nonlinear systems based on the Ordinary Kriging (OK) metamodel. The OK is used to build a set of Nonlinear Autoregressive models with Exogenous inputs (NAREX), able to approximate the system future outputs as a function of previous inputs and outputs considering a specific delay. Then, these OK-based dynamic models are used in a recursive interactive interpolations scheme to predict the process outputs over several time steps. The capabilities of the OK-based dynamic models are compared to other leading techniques, via their application to benchmark cases. The application results reveal the OK promising and competitive capabilities for MVDM of nonlinear systems, in terms of prediction accuracy and prediction time horizon.

引用

页码：265 / 270

页数：6

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