东南大学学报（英文版）2020，Vol.36Issue(1)：41-49,9.DOI:10.3969/j.issn.1003-7985.2020.01.006
一种新型异构集成极端学习机模型及其软测量应用
A novel heterogeneous ensemble of extreme learning machines and its soft sensing application
摘要
Abstract
To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data, an effective heterogeneous ensemble of extreme learning machines ( HEELM ) is proposed. Specifically, the kernel extreme learning machine ( KELM) and four common extreme learning machine ( ELM) models that have different internal activations are contained in the HEELM for enriching the diversity of sub-models. The number of hidden layer nodes of the extreme learning machine is determined by the trial and error method, and the optimal parameters of the kernel extreme learning machine model are determined by cross validation. Moreover, to obtain the best output of the ensemble model, least squares regression is applied to aggregate the outputs of all individual models. Two complex data sets of practical industrial processes are used to test the HEELM performance. The simulation results show that the HEELM has a high prediction accuracy. Compared with the individual ELM models, bagging ELM ensemble model, BP and SVM models, the prediction accuracy of the HEELM model is improved by 4. 5% to 8. 7% , and the HEELM model can obtain better generalization capability.关键词
软测量/极端学习机/最小二乘/集成Key words
soft sensor/extreme learning machine/least squares/ensemble分类
能源科技引用本文复制引用
马宁,董泽..一种新型异构集成极端学习机模型及其软测量应用[J].东南大学学报（英文版）,2020,36(1):41-49,9.基金项目
The National Natural Science Foundation of China ( No. 71471060 ) , the Natural Science Foundation of Hebei Province ( No. E2018502111) , Fundamental Research Funds for the Central Uni-versities ( No. 2019QN134) . （ No. 71471060 ）
