摘要
提出一种新的基于模糊聚类和卡尔曼滤波方法的模糊辨识算法 .该方法是基于快速模糊聚类 ,计算给定样本在各类中的隶属度 ,并利用卡尔曼滤波方法辨识模糊模型的结论参数 .整个辨识过程与一般的模糊聚类方法 [1 ]相比 ,需要的 CPU时间大大缩短 .最后通过仿真实例验证了该方法的有效性 .
A method of fuzzy identification based on fuzzy clustering and Kalman filtering is proposed. The membership degree of each given pattern is calculated by fast fuzzy clustering algorithm. Kalman filtering can be used to identify the consequent parameters. The CPU time has slowed down sharply compared with the common fuzzy clustering method \ . Finally, the effectiveness of this method is demonstrated by the simulation result.
出处
《数学的实践与认识》
CSCD
北大核心
2003年第3期17-22,共6页
Mathematics in Practice and Theory
