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.
出处
《华东理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2004年第6期694-697,共4页
Journal of East China University of Science and Technology
基金
国家863计划项目(2002AA412120)
关键词
减法聚类
T—S模糊模型
泛化能力
软测量
聚类半径
subtractive clustering
T-S fuzzy model
generalization capability
soft sensor
radius of a cluster center
