摘要
针对传统的中长期模糊聚类预测算法自变量权重选择不合理、截水平集合元素不全面、相关因子计算方法单一等缺陷,提出改进的预测算法。该算法利用关联度分析计算自变量权重,通过建立相关因子计算方法库,按照相对传递总偏差最小原则选择最佳相似矩阵进行聚类,以等价矩阵所有元素的去重集合作为截水平集合求最佳聚类。实验结果证明该算法可提高预测的准确性。
Classical fuzzy clustering algorithm has some drawbacks including that the computing of independent variable weights is unreasonable, the set of horizontal section members is slurred, the computational methods of correlation factor are single and so on. In order to solve the problems above, this paper proposes a new algorithm named improved fuzzy clustering algorithm. It uses association analysis to compute the independent variable weights, sets up a method warehouse and uses it to calculate the correlation factors, and selects distinct members of the equivalent matrix as the set of horizontal section. Experimental result demonstrates that the new algorithm increases the accuracy of forecast.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第15期184-186,共3页
Computer Engineering
关键词
模糊聚类
相关因子
相似矩阵
关联度分析
中长期电力负荷预测
fuzzy clustering
correlation factor
similar matric
association analysis
forecast of middle and long term electric power load
