模糊模型辨识中模糊聚类方法应用分析

Analysis on the Applications of Fuzzy Clustering Method in Fuzzy Model Identification

模糊聚类算法已广泛应用于模式识别、数据聚类以及从数据中提取模糊规则的过程。介绍了基于模糊聚类的非线性系统模糊辨识方法 ,并通过著名的Box和Jenkins煤气炉数据仿真实例详细研究了模型性能指标与输入变量及模糊聚类数之间的关系 ,指出了应用模糊聚类方法的优势与不足。

The fuzzy clustering algorithm has been extensively used for pattern recognition and data clustering. It has also been applied in the process of generating fuzzy rules from data. This paper introduces a nonlinear system fuzzy identification method based on fuzzy clustering, and studies the relation between the model is performance index and the number of input variables and fuzzy clustering centers in detail through the simulation of the famous Box-Jenkins gas furnace data. It also presents the advantages and disadvantages of using fuzzy clustering method. It is of great importance for the applications of fuzzy clustering method in fuzzy modeling.