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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Abstract

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

Item Type: Journal Article
Journal or Publication Title: IEEE Transactions on Fuzzy Systems
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Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 907
Deposited By: Dr. Plamen Angelov
Deposited On: 09 Jan 2008 09:18
Refereed?: Yes
Published?: Published
Last Modified: 24 Aug 2019 00:47
URI: https://eprints.lancs.ac.uk/id/eprint/907

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