文献类型：期刊文献

英文题名：ADAPTIVE SAMPLING FOR SURROGATE MODELLING WITH ARTIFICIAL NEURAL NETWORK AND ITS APPLICATION IN AN INDUSTRIAL CRACKING FURNACE

作者：Jin, Yangkun[1];Li, Jinlong[2];Du, Wenli[1];Qian, Feng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：94

期号：2

起止页码：262

外文期刊名：CANADIAN JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20160401854873);WOS:【SCI-EXPANDED(收录号:WOS:000370195600008)】；

基金：This work is supported by the National Natural Science Foundation of China (U1162202,21276078), the Shanghai Municipal Science and Technology Commission (13111103800), the Natural Science Foundation of Shanghai (13ZR1411300), the "Shu Guang" project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation, and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：surrogate modelling; adaptive sampling; artificial neural network; hidden node number

摘要：In surrogate modelling, a simple functional approximation of a complex system model is always constructed to reduce the computational expense, and the selection of a suitable surrogate model and a sampling method are key to obtaining a surrogate model for a complex system. To construct an appropriate surrogate model, three methods of adaptive surrogate modelling that use artificial neural networks (ANN) are developed by incorporating a new mechanism for automatically determining the number of hidden nodes and/or a new prediction error-based mixed adaptive sampling method. In the automatic determination, the number of hidden nodes can adaptively change according to the effective rate of parameters in the ANN during the adaptive surrogate modelling process. As a result, an improper number of hidden nodes determined by the empirical method can be avoided. The prediction error-based mixed adaptive sampling method is capable of finding the strong nonlinear behaviour of the underlying system, which is easily missed by the traditional prediction variance-based sampling method. The three methods and the previous method for adaptive surrogate modelling that use ANN are tested and compared in terms of replicating the behaviours of three types of challenge functions to determine the efficacy of the developed methods. Furthermore, these methods are used in an engineering problem of surrogate modelling for a cracking reaction simulator to validate the efficacy of the developed methods.