摘要
电力系统数据信息处理与应用领域广泛涉及数据挖掘与特征提取问题,为了提高其中聚类算法的有效性,提出了一种粒子群优化算法(PSO)与模糊C均值算法(FCM)有机结合的粒子群优化模糊聚类算法。该算法用PSO优化过程代替FCM中的基于梯度下降的迭代过程,充分利用PSO具有全局寻优、快速收敛的特点,使算法具有很强的全局搜索能力,有效地避免了FCM易陷入局部极小的缺陷;同时也降低了FCM对初始值的敏感度。还通过核方法,将低维特征空间的样本通过核函数映射到高维特征空间,增强了特征的优化,使特征在高维空间更易聚类。电力系统负荷样本聚类的应用仿真研究结果表明:与单纯FCM法相比,该算法聚类更准确,效果更佳。
Information process and its application of power system relate widely to the technology of data mind and character extraction. In order to increase the efficiency of clustering algorithm, a novel clustering algorithm of power system, which combines particle swarm optimization （PSO） with fuzzy C means （FCM） organically, is presented. The algorithm uses PSO to replace the iteration process of FCM based on the gradient descent. So it has strong global searching capacity, and avoids the problems of local optimization and initialization values. At the same time, the kernel method is involved to map the eigenspace of low dimensions to that of high dimensions by kernel function, so the optimization of eigenvalue is enhanced, and the eigenvalue is easier to be clustered in the eigenspace of high dimension. The simulation result of power system load clustering shows that the proposed algorithm is more accurate and efficient than FCM.
出处
《继电器》
CSCD
北大核心
2007年第22期40-44,共5页
Relay
关键词
模糊聚类
数据挖掘
粒子群优化
负荷样本
fuzzy clustering
data mining
particle swarm optimization
load sample
