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

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

Content Provider	Springer Nature Link
Author	Soleimani B., Hossein Lucas, Caro Araabi, Babak N.
Copyright Year	2010
Abstract	A novel online learning approach for neuro-fuzzy 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.
Starting Page	59
Ending Page	71
Page Count	13
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	1
Issue Number	1
e-ISSN	18686486
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-07-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Evolving neuro-fuzzy model (ENFM) Modeling time varying systems Recursive Gath–Geva clustering Online adaptive learning Time series prediction Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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