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中文摘要：
针对模糊C均值聚类算法（FCM）中聚类结果受初始聚类中心影响突出的缺陷,利用粒子群优化算法（PSO）全局优化能力显著的特性,提出一种基于粒子群改进的模糊C均值聚类算法（PSO-C-FCM）.该算法首先通过PSO优化算法得到一个最优值,然后利用该最优值初始化FCM聚类中心,从而优化了FCM算法的聚类结果.最后将该算法应用于电机故障诊断中,实验表明,该算法弥补了FCM算法的缺陷,提高了聚类的效率和准确性,改善了故障诊断结果.
英文摘要：
In order to overcome the Fuzzy C-means Algorithm’s defects of sensitivity to the initial cluster centers, using the efficient global optimization characteristics of the PSO algorithm, this paper proposes a new PSO-based fuzzy algorithm （PSO-C-FCM）. It first finds the optimal extreme using the PSO algorithm, and then initializes the cluster centers of FCM algorithm with the optimal extreme, which makes the algorithm more efficient and accurately. This new algorithm is applied to the motor fault diagnosis, and experiments show that the algorithm makes up the defects of Fuzzy C-means Algorithm, improves the efficiency and accuracy of fuzzy clustering, and improves the fault diagnosis.
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