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An Approach for Fuzzy Rule-base Adaptation using On-line Clustering.

Angelov, Plamen (2004) An Approach for Fuzzy Rule-base Adaptation using On-line Clustering. International Journal of Approximate Reasoning, 35 (3). pp. 275-289. ISSN 0888-613X

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Abstract

A recursive approach for adaptation of fuzzy rule-based model structure has been developed and tested. It uses on-line clustering of the input–output data with a recursively calculated spatial proximity measure. Centres of these clusters are then used as prototypes of the centres of the fuzzy rules (as their focal points). The recursive nature of the algorithm makes possible to design an evolving fuzzy rule-base in on-line mode, which adapts to the variations of the data pattern. The proposed algorithm is instrumental for on-line identification of Takagi–Sugeno models, exploiting their dual nature and combined with the recursive modified weighted least squares estimation of the parameters of the consequent part of the model. The resulting evolving fuzzy rule-based models have high degree of transparency, compact form, and computational efficiency. This makes them strongly competitive candidates for on-line modelling, estimation and control in comparison with the neural networks, polynomial and regression models. The approach has been tested with data from a fermentation process of lactose oxidation. (c) 2003 Elsevier Inc. All rights reserved.

Item Type: Article
Journal or Publication Title: International Journal of Approximate Reasoning
Additional Information: The final, definitive version of this article has been published in the Journal, International Journal of Approximate Reasoning 35 (3), 2004, © ELSEVIER.
Uncontrolled Keywords: On-line clustering ; Fuzzy rule-based models identification ; Parameter estimation ; Takagi–Sugeno fuzzy models ; DCS-publications-id ; art-572 ; DCS-publications-credits ; dsp-fa ; DCS-publications-personnel-id ; 82
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 900
Deposited By: Dr. Plamen Angelov
Deposited On: 08 Jan 2008 14:45
Refereed?: Yes
Published?: Published
Last Modified: 21 Mar 2014 10:13
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/900

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