We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK


93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > An Approach for Fuzzy Rule-base Adaptation usin...
View graph of relations

« Back

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

Research output: Contribution to journalJournal article


Journal publication date03/2004
JournalInternational Journal of Approximate Reasoning
Number of pages15
Original languageEnglish


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.

Bibliographic note

The final, definitive version of this article has been published in the Journal, International Journal of Approximate Reasoning 35 (3), 2004, © ELSEVIER.