基于二阶模糊聚类算法的雷达目标距离像识别

针对于模糊C-均值(FCM)算法敏感于聚类中心初始值的缺点，提出一种基于二阶模糊聚类方法。该方法利用传递闭包(TC)算法无初始化的优点，先对样本集按一定分类水平进行划分，选取若干类，求得这些类的样本均值作为FCM算法的初始聚类中心。一方面能够获得理想的聚类中心初始值，同时还能通过分类水平值来优化聚类中心数和聚类中心，避免局部最优，克服一致性聚类。利用该算法对三类飞机目标的实测一维距离像数据进行了识别实验，实验结果表明，基于二阶模糊聚类方法的识别率比FCM有了明显的改善。

Range profile target recognition based on second-order fuzzy clustering

Concerning that fuzzy C-means (FCM) algorithm was sensitive to the initial value of cluster centers, a fuzzy clustering method based on Second-Order Fuzzy Clustering (SOFC) was proposed, which took advantage of Transitive Closure (TC) algorithm's non-initialization, the samples were classified according to certain classification level firstly, and then the number of class was selected. The sample means of these classes was used to initialize the FCM algorithms cluster centers. On one hand, it can obtain good initial value of cluster centers; on the other hand, the level of value through the categories can optimize the number of cluster centers and cluster centers, avoiding local optimum and overcoming the consistency of clustering. The algorithm was used to recognize three types of aircraft targets based on one dimensional image data. The experimental results show that the recognition of SOFC algorithm gets obvious improvement than FCM algorithm.