Recursive Gath-Geva clustering as a basis for evolving neuro-fuzzy modeling

A recursive extension of Gath-Geva clustering algorithm is proposed in this paper which is used as a basis for online tuning and development of neuro-fuzzy models. In comparison with other online modeling approaches which use spherical clusters for defining validity region of neurons, the proposed evolving neuro-fuzzy model (ENFM) has the ability to take advantage of elliptical clusters. This extension increases the ability of local linear neurons of ENFM to capture system behavior in more sophisticated regions which leads to decrease in number of neurons as well as increase in the modeling ability. The proposed model is capable of adapting to changes in system behavior by adding new neurons or merging similar existing fuzzy rules. Efficiency of evolving neuro-fuzzy model is investigated in prediction of Mackey-Glass and smoothed sunspot number time series. Results of these simulations show better performance of the proposed model as compared with other online modeling approaches.

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