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

Content Provider	IEEE Xplore Digital Library
Author	Soleimani-B, H. Lucas, C. Araabi, B.N.
Copyright Year	2010
Description	Author affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, POB: 14345-515, Tehran, Iran (Soleimani-B, H.; Lucas, C.; Araabi, B.N.)
Abstract	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.
Starting Page	1
Ending Page	7
File Size	629465
Page Count	7
File Format	PDF
ISBN	9781424469192
ISSN	10987584
e-ISBN	9781424469215
DOI	10.1109/FUZZY.2010.5584088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Neurons Covariance matrix Time series analysis Mathematical model Computational modeling Adaptation model
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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