基于模糊聚类多模型的感应电机建模——实时验证

A. Aicha, Bnhamed Mouna, S. Lassâad
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引用次数: 2

摘要

本文讨论了基于多模型方法的两种建模方法的比较研究。第一种是基于c均值聚类算法,第二种是基于k均值聚类算法。将这两种方法实验应用于感应电动机。多模型建模包括通过有限数量的局部模型来表示IM。这个模型的数量最初必须是固定的,因此需要一个减法聚类。然后利用c均值和k均值聚类来确定聚类。然后,这些集群将在结构和参数识别的基础上被利用,以确定组合在一起的局部模型,最终形成多模型。实验研究基于MATLAB/SIMULINK环境,采用DS1104控制板的DSpace方案。实验结果表明,基于K-means的多模型聚类算法是最有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation
This paper discusses a comparative study of two modeling methods based on multimodel approach. The first is based on C-means clustering algorithm and the second is based on K-means clustering algorithm. The two methods are experimentally applied to an induction motor. The multimodel modeling consists in representing the IM through a finite number of local models. This number of models has to be initially fixed, for which a subtractive clustering is necessary. Then both C-means and K-means clustering are exploited to determine the clusters. These clusters will be then exploited on the basis of structural and parametric identification to determine the local models that are combined, finally, to form the multimodel. The experimental study is based on MATLAB/SIMULINK environment and a DSpace scheme with DS1104 controller board. Experimental results approve that the multimodel based on K-means clustering algorithm is the most efficient.
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