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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 Function) 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.Abstract: 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.
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Key words:
- Soft sensors /
- neural network /
- adaptive fuzzy clustering /
- reheating furnace /
- slab temperature
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