基于加权双质心支持向量聚类的机群编队分组

针对机群编队分组问题,提出了一种加权双质心支持向量聚类算法。所提算法在支持向量训练时引入最大熵原理,快速求解Lagrange乘子;针对样本特征对聚类结果的贡献不同,在聚类标识过程中,引入加权密度质心,提出了加权双质心聚类标识,并在典型数据集上验证了所提算法的有效性。通过对机群编队分组模型的描述,建立了机群聚类时一个目标点需要的特征集,完成了编队分组的仿真实验。仿真结果表明了所提算法能够针对应用的具体样本集实行快速聚类分析,并保证聚类结果的有效性。

Double centroids-weighted support vector clustering algorithm for group-air grouping

Aiming at group air grouping,a double centroids weighted support vector clustering(D-SVC) algorithm is proposed. To effectively solve the Lagrange multipliers during the support vector machine training, the proposed algorithm introduces the maximum entropy principle. Different characteristics of samples lead to different clustering results,so the density weighted centroids is introduced to the double centroids weighted cluster labeling process, and the effectiveness of the algorithm is proved in classical sample sets. Through describing the group air grouping model, the feature sets of a target point are proposed during group air grouping. Besides, simulation experiment for group air grouping is carried out. Simulation results show that the D-SVC cluster analysis of specific sample sets can be carried out quickly using the proposed algorithm which also ensures the effectiveness of the clustering results.