An approach to Online identification of Takagi-Suigeno fuzzy models

被引:739
作者
Angelov, PP [1 ]
Filev, DP
机构
[1] Univ Lancaster, Dept Commun Syst, Lancaster LA1 4YR, England
[2] Ford Motor Co, Detroit, MI 48239 USA
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2004年 / 34卷 / 01期
关键词
online recursive identification; rule-base adaptation; Takagi-Sugeno models;
D O I
10.1109/TSMCB.2003.817053
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.
引用
收藏
页码:484 / 498
页数:15
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