摘要
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.
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.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2014年第5期1283-1289,共7页
Systems Engineering-Theory & Practice
基金
国家高技术研究发展计划(863计划)(2011AA05A116)
国家自然科学基金(71131002
71071045)
