摘要
为使T-S模型在线辨识时能够更加合理地划分模糊空间,提出一种根据相邻聚类中心距离确定模糊空间重叠系数的方法,将该方法与一次完成最小二乘法、递推最小二乘法相结合,得到了一种辨识精度较高的T-S模型在线辨识算法,以某型号单晶炉热场的实际运行数据为对象,应用所提出的算法对热场模型进行在线辨识,辨识结果表明,由该辨识算法得到的单晶炉热场模型具有较高的精度。
To more reasonably partition fuzzy spaces during online identification of T-S model, a calculation method on overlap coefficient betweeh two fuzzy spaces is proposed. In this method, the overlap coefficient can be derived by the centre distance between two contiguous clusters. In addition, an online T-S model identification algorithm which has higher identification accuracy can be obtained through the integration of this method, least square（LS） algorithm and recursive least square（RLS） algorithm. Based on the data of thermal field from a single crystal furnace, the thermal field model is on-line identified by this identification algorithm. Simulation results show that the single crystal furnace thermal field model identified by this method has higher precision.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第9期1425-1428,1432,共5页
Control and Decision
基金
国家科技重大专项资金项目(2009ZX02011001)
关键词
T-S模型
在线辨识
自适应重叠系数
聚类
最小二乘
单晶炉热场
T-S model, online identification adaptive overlap coefficient, cluster, least square, single crystal furnacethermal field
