Recursive (G)ath-Geva clustering as a basis for evolving neuro-fuzzy modeling

A novel online learning approach for neurofuzzy models is proposed in this paper. Unlike most of the previous online methods which use spherical clusters to define validity region of neurons, the proposed learning method is based on a recursive extension of Gath-Geva clustering algorithm, which is capable of constructing elliptical clusters as well. Eliminating the constraint of spherical clusters by considering general structures for covariance matrices, empowers the proposed evolving neuro-fuzzy model (ENFM) to capture more sophisticated behaviors with less modeling error as well as fewer number of neurons. The proposed recursive clustering method has the ability to cluster data streams using online identification of number of required clusters and recursive estimation of cluster parameters. A merging strategy is also proposed to merge similar clusters which consequently hinders the model from having excessive number of neurons with similar behaviors. Applicability of ENFM is also investigated in modeling a time varying heat exchanger system and prediction of Mackey-Glass and sunspot numbers time series. Simulation results indicate better performance of the proposed model as compared with that of several well-known modeling and prediction methods.