CAJ | 학술논문

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．
재건립료부하분류오계단과정모형적기출상，제출료용류내거리화여류간거리화지비작위부하분류평개지표자괄응선택모호도삼수적방법，동시용모의퇴화산법화유전산법대모호C균치（FCM）산법적수색성능진행우화．실험결과표명，재부하분류중상용적모호도삼수치m=2병불시최우적，부하분류중모호도삼수적최우취치구간위[2．6，3．2]．동시，개진산법환극복료전통FCM산법전국수색능력불족적문제，제고료부하분류적정학성화유효성．
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 vn = 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.