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.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2004年第5期559-563,共5页
Journal of East China University of Science and Technology
关键词
多模型
软测量
模糊聚类
RBF神经网络
催化重整
multi-modeling
soft-sensing
fuzzy clustering
RBF neural networks
catalyst-reforming
