基于模糊规则的非线性系统快速建模方法  

Nonlinear Systems Fast Modeling Based on Fuzzy Logic Rules

作　　者：刘福才[1] 关新平[2] 裴润[1] 

机构地区：[1]哈尔滨工业大学控制科学与工程系,哈尔滨150001 [2]燕山大学电气工程学院自动化系,秦皇岛066004

出　　处：《高技术通讯》2003年第7期74-78,共5页Chinese High Technology Letters

基　　金：国家杰出青年基金 ( 6992 5 3 0 8);黑龙江省自然科学基金资助项目

摘　　要：描述了一个通过学习从输入输出采样数据中提取MISO模糊规则的具有插值性能的新型模糊系统。提出的模糊模型继承了Sugeno模型及其变化形式的许多优点。对于新的模糊模型采用递推最小二乘估计方法代替启发式误差反馈学习方法辨识结论参数 ,整个辨识过程所需的计算时间相对于其他方法最短 ,适用于在线辨识。This article describes a new type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input output sample data through learning. The proposed model inherits many merits from Sugeno type models and their variations. Using recursive least square estimation method take the place of a heuristic error feedback learning algorithm to identify consequence parameters about the new model suggested. The whole identification process takes much less computation time than other method, suitably on line identification. The simulation results demonstrate that the proposed method is effectiveness and practicability.

关 键 词：模糊规则 非线性系统 Sugeno模型 三角形隶属函数 参数辩识 递推最小二乘估计 非线性SISO系统 MISO 

分 类 号：O159[理学—数学]