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中文摘要：
针对现有直觉模糊核 c-均值（intuitionistic fuzzy kernel c-means，IFKCM）聚类算法对初始值敏感、易陷入局部最优解及收敛速度慢等缺陷，汲取了粒子群优化（particle swarm optimization，PSO）算法优势，对初始聚类中心进行优化，提出了基于粒子群优化的直觉核 c-均值（particle swarm-based intuitionistic fuzzy kernel c-means，PS-IFKCM）聚类算法，选取4组标准数据集实际样本数据对算法的有效性进行了试验。最后选取弹道中段目标识别常用的雷达截面积（radar cross section，RCS）这一特征属性进行弹道中段目标识别仿真实验，并将其与模糊 c-均值（fuzzy c-means，FCM）算法、IFKCM 算法的识别效果及运行时间进行比较分析，表明了该算法应用于弹道中段目标识别的有效性及优越性。
英文摘要：
The intuitionistic fuzzy clustering algorithms are sensitive to the initial value,easy to fall into local opti-mum and have slow convergence speed.To overcome these shortages,the particle swarm optimization（PSO）algorithm with powerful ability of global search and quick convergence rate is applied to intuitionistic fuzzy clustering.Firstly, PSO is used to optimize the initial clustering centers.Then,the approach of intuitionistic fuzzy kernel c-means（IFKCM） based on PSO,namely PS-IFKCM,is proposed.Then,experiments based on four measured datasets are carried out to illustrate the performance of the proposed method.Subsequently,the tactical ballistic missile （TBM）target recognition experiment is carried out based on radar cross section （RCS）,which is usually applied in target rec-ognition in the middle ballistic trajectory.Compared with results from fuzzy c-means and IFKCM,PS-IFKCM is of great efficiency when it comes to target recognition in the middle ballistic trajectory.
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