文献类型：期刊文献

中文题名：神经网络多模型软测量技术及应用

英文题名：Multi-modeling Soft-sensing Technique and Its Application Based on Neural Network

作者：高林[1];顾幸生[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2004

卷号：30

期号：5

起止页码：559

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;EI(收录号：2004498707051);Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：多模型;软测量;模糊聚类;RBF神经网络;催化重整

外文关键词：multi-modeling; soft-sensing; fuzzy clustering; RBF neural networks; catalyst-reforming

摘要：基于多模型思想,采用模糊聚类的方法对软测量数据进行了分类,对每类数据基于神经网络(NN)建模,采用RBF神经网络构造了每个数据样本的隶属度,将各模型输出的数据进行隶属度加权求和得到最终的软测量输出,并对某催化重整生产装置催化剂再生器氧含量进行了建模研究,获得了满意的结果。
Based on multi-modeling idea, fuzzy clustering method is used to classify soft-sensing data. For each class, different modeling methods based on artificial neural network are used. Furthermore, RBF neural network is used to build the degree of membership of every sample in this paper. The degrees of membership are used for combing several models to obtain the final result. The method is applied to model a practical case of oxygen content of catalyst-reforming process in petrol refining.