基于最小二乘支持向量机的T-S模型在线辨识

基金项目: 上海高校选拔培养优秀青年教师科研专项基金资助项目（21020）；上海理工大学博士科研启动基金资助项目（X641）

详细信息

LSSVM-Based Online Identification for T-S Model

College of Computer and Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

摘要: 提出一种基于时间窗最小二乘支持向量机的T-S模型在线辨识算法，包括结构辨识和参数辨识．该算法以时间窗内数据的势能作为结构辨识依据，同时采用最小二乘支持向量机辨识系统参数，具有辨识速度快、精度高的特点．仿真结果证明了算法的有效性．
- T-S模型 /
- 时间窗 /
- 势能 /
- 最小二乘支持向量机
Abstract: An online identification method based on least square support vector machine (LSSVM) with time window for T-S model is proposed,including structure and parameter identifications.Structure identification depends on the potential of data in the time window,and parameter identification is based on LSSVM,resulting in high identification speed and precision.The simulation result illustrates the effectiveness of the proposed method.
- T-S model /
- time window /
- potential /
- least square support vector machine (LSSVM)

[1]	阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真[J].上海交通大学学报,2004,38(4):524～526,532.
[2]	Buckley J J.Sugeno type controllers are universal controllers[J].Fuzzy Sets and Systems,1993,53(3):299.
[3]	Chiu S L.Fuzzy model identification based on cluster estimation[J].Journal of Intelligent and Fuzzy Systems,1994,2(3):267～278.
[4]	Qi R Y,Brdys M A.Adaptive fuzzy modelling and control for discrete-time nonlinear uncertain systems[A].Proceedings of the 2005 American Control Conference[C].Piscataway,NJ,USA:IEEE,2005.1108～1113.
[5]	Vapnik V N.The Nature of Statistical Learning Theory[M].New York,USA:Springer-Verlag,1995.
[6]	Suykens J A K,Vandewale J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293～300.
[7]	Takagi T,Sugeno M.Fuzzy identification of systems and its application to modeling and control[J].IEEE Transactions on Systems,Man,and Cybernetics,1985,SMC-15(1):116～132.