水泥熟料质量等级的半监督模糊聚类建模方法

Semi-supervised Fuzzy Clustering Method of Modeling Quality Grade of Cement Clinker

运用半监督模糊聚类算法抽取了水泥熟料质量等级和生产过程中工艺参数之间的对应关系,在此基础上建立了规则化的熟料质量等级模型;具体算法上,引入了两类监督信息来改进无监督模糊聚类算法:一类是成对约束数据,目的是为了降低数据维度和改善空间相似性,一类是标签数据,目的是为了初始化聚类中心和修正聚类目标函数;经实际生产数据验证,改进后的算法可有效提高建模准确率、降低聚类维度和缩短计算时间。

Semi-supervised fuzzy clustering algorithm is used to find the relationship between the quality grade of cement clinker and the technological parameters of production process. On this basis, the discrete model of clinker quality grade is built in the form of rule sets. On the specific algorithm, two monitoring information types are introduced to improve the unsupervised fuzzy clustering： One type is the pairwise constraints, the purpose is to reduce dimension of the data and improve similarity of the spatial. The other type is the labeled samples, the purpose is to initialize the cluster center and make some modification of the objective function. Experimental on real production data demonstrate that the improved algorithm can effectively improve the predict accuracy, reduce the clustering dimension and the computation time.