文献类型：期刊文献

中文题名：基于减法聚类产生具有优化规则的模糊神经网络及其软测量建模

英文题名：Generating Fuzzy-neural Networks with Optimal Fuzzy Rules Based on Subtractive Clustering with Applications to Soft Sensor Modeling

作者：杨红卫[1];李柠[1];侍洪波[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：6

起止页码：694

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：2005068830089);Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

基金：国家863计划项目(2002AA412120)

语种：中文

中文关键词：减法聚类;T—S模糊模型;泛化能力;软测量;聚类半径

外文关键词：subtractive clustering; T-S fuzzy model; generalization capability; soft sensor; radius of a cluster center

摘要：提出了一种通过调整减法聚类半径优选模糊规则的软测量建模方法。首先用减法聚类建立T-S模糊模型,然后通过调整聚类半径优选模糊规则数,以取得具有良好泛化性能的模型,之后利用梯度下降混合最小二乘算法精调参数。最后用该方法对初馏塔石脑油干点进行软测量建模,结果表明能较快确定优化模型,并能满足软测量建模精度要求。
A soft sensor modeling method is presented which selects optimal fuzzy rules by tuning the radius of a subtractive cluster center. Subtractive clustering is used to generate a T-S fuzzy model. Secondly, the radius of a cluster center is adjusted to select optimal fuzzy rules, to acquire a fuzzy model with perfect generalization capability. The parameters is fine-tuned by means of a hybrid gradient descent (GD) and least-squares estimation (LSE). Finally, the method is used to model a PDU naphtha's dry point and the result shows that it can determine the optimal model fastly and achieve satisfactory prediction precision.