Data-Driven Fuzzy Modeling For Nonlinear dynamic System

摘要：

In this paper, A new method for dynamic learning of Takagi-Sugeno (T-S) model based on input-output data is presented. It is based on a novel learning algorithm that recursively updates T-S model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the T-S model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. To reduce the complexity of fuzzy models while keeping good model accuracy, orthogonal least squares (OLS) method algorithm is used to remove redundant fuzzy rules,at the sametime the consequent parameters of the T-S model are identified and optimized. The approach has been successfully applied to T-S models of non-linear dynamical system modeling.

关键词： Takagi-Sugeno Model Fuzzy Clustering orthogonal least squares

作者: Hao Wan-Jun Qiao yan-Hui Zhu Xue-Li Li Ze

作者单位: Suzhou University of sience and tecnology,Suzhou 215011 china;Beihua University, Jilin 132021, China Suzhou University of sience and tecnology, Suzhou 215011 china;Beihua University, Jilin 132021, Chin

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 1095-1100

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）