Identification of Evolving Rule-based Models.

Angelov, Plamen and Buswell, Richard (2002) Identification of Evolving Rule-based Models. IEEE Transactions on Fuzzy Systems, 10 (5). pp. 667-677. ISSN 1063-6706

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An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy Systems

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IEEE Transactions on Fuzzy Systems
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Uncontrolled Keywords:
?? adaptive nonlinear control air-conditioning component modeling behavior modeling complex processes evolving fuzzy rule-based models fault detection fault diagnostics forecasting fuzzy rules identification incremental unsupervised learning informative pote ??
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09 Jan 2008 09:18
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02 May 2024 23:43