摘要
为解决FCM算法对初始值敏感而易于陷入局部极小点的问题,针对FCM算法应用于系统原位测试时小数据量特点,提出了一种增量方式全局最优模糊c均值算法,进行了收敛速度优化并给出了算法步骤,机载武器系统信息通道原位故障诊断实验验证了此算法在小数据量情况下可以较好地解决FCM算法收敛局部最优的问题。
In order to solve the problem apt to land in local minimum results for its sensitivity to the initial conditions, in view of character of test - online, this paper proposes a global fuzzy c - means （GFCM） clustering algorithm based on incremental approach to clustering. The converging speed of GFCM is improved by simplifying the algorithm and then the approach of the algorithm is given. The experimental test for unsupervised clustering and fault pattern recognition of the information channels of airborne weapon system is given by using the new GFCM algorithm. The results show that the proposed algorithm is effective in dealing with the aforementioned problem under condition of small data capacity.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2007年第6期16-18,共3页
Journal of Air Force Engineering University(Natural Science Edition)
基金
军队科研基金资助项目
关键词
聚类
模糊C均值
全局优化
故障诊断
cluster
fuzzy c - means
global optimization
fault diagnosis
