An approach to online identification of Takagi-Sugeno fuzzy models

Angelov, Plamen and Filev, Dimitar (2004) An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 34 (1). pp. 484-498. ISSN 1083-4419

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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.

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Journal Article
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IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics
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Uncontrolled Keywords:
?? adaptive nonlinear control behavior modeling evolving takagi-sugeno fuzzy model fault detection fuzzy rules knowledge extraction neural networks online learning online recursive identification robotics rule-base adaptation unsupervised learning dcs-public ??
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08 Jan 2008 14:37
Last Modified:
10 Jan 2024 00:13