摘要
研究基于模糊聚类的钢坯温度神经网络软测量模型．该方法由两个部分组成，FCM(Fuzzy C-Means)聚类算法用来对训练样本进行分类，分布式 RBF(Radial Basis Func-tion)网络对每类样本进行训练．在线测量时，采用自适应模糊聚类算法对新的工况数据进行隶属度计算．文中将该算法应用于步进式加热炉钢坯温度的预报，仿真结果表明该算法的有效性．
A slab temperature neural network soft sensor model based on fuzzy clustering is studied. The approach consists of two components: an FCM (Fuzzy C-Means) clustering, which classifies training objects into a couple of clusters, and a distributed RBF (Radial Basis Function) network, which is used to train each cluster. In the online stage, the values of membership are computed using an adaptive fuzzy clustering algorithm for the new object. The proposed approach has been applied to the slab temperature estimation in an actual reheating furnace. Simulations show that the approach is effective.
出处
《自动化学报》
EI
CSCD
北大核心
2004年第6期928-932,共5页
Acta Automatica Sinica
基金
国家自然科学基金(69934020
60074004)上海市高校科技发展基金(04FA02
03IK09)资助~~
关键词
软测量
神经网络
自适应模糊聚类
加热炉
钢坯温度
Algorithms
Computer simulation
Estimation
Mathematical models
Neural networks
Temperature control
