基于自适应模糊度参数选择改进FCM算法的负荷分类

在建立了负荷分类五阶段过程模型的基础上，提出了用类内距离和与类间距离和之比作为负荷分类评价指标自适应选择模糊度参数的方法，同时用模拟退火算法和遗传算法对模糊C 均值（FCM） 算法的搜索性能进行优化. 实验结果表明，在负荷分类中常用的模糊度参数值m=2并不是最优的，负荷分类中模糊度参数的最优取值区间为[2.6，3.2]. 同时，改进算法还克服了传统 FCM 算法全局搜索能力不足的问题，提高了负荷分类的精确性和有效性.

Load classification based on improved FCM algorithm with adaptive fuzziness parameter selection

This paper proposes an adaptive fuzziness parameter selection method of fuzzy c-means (FCM) algorithm based on the establishment of five-stage load classification process model. The evaluation index of adaptive fuzziness parameter selection is the ratio of the sum of within-class distances and the sum of between-class distances. At the same time, simulated annealing algorithm and genetic algorithm are utilized to optimize the global search capability of FCM algorithm. Experimental results show that the widely used fuzziness parameter of FCM algorithm in load classification m=2 is not optimal, and we give the optimum range that is [2.6, 3.2]. The modified algorithm enhances the global search capability of traditional FCM algorithm, thus enhancing the accuracy and effectiveness of load classification.