A Compensatory NeuroFuzzy System with Online Constructing and Parameter Learning

Ming-Feng Han, Chin-Teng Lin, Jyh Yeong Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A compensatory neurofuzzy system (CNFS) with on-line learning ability is proposed in this paper. The proposed CNFS model uses a compensatory layer to raise the diversity of fuzzy rules by compensatory weights. The compensatory layer can automatically compare with each fuzzy rule and select higher resources for more important fuzzy rule. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the fuzzy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the weights of the compensatory layer. To demonstrate the capability of the proposed CNFS, it is applied to the Iris, and Wisconsin breast cancer classification datasets from the UCI Repository. Experimental results show that the proposed CNFS for pattern classification can achieve good classification performance.
Original languageAmerican English
Title of host publication2010 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010)
PublisherIEEE
ISBN (Print)978-1-4244-6588-0
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 - Istanbul, Turkey
Duration: 10 Oct 201013 Oct 2010

Publication series

Name IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
PublisherIEEE
ISSN (Print)1062-922X

Conference

Conference2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
CountryTurkey
CityIstanbul
Period10/10/1013/10/10

Keywords

  • Compensatory NeuroFuzzy System (CNFS); NeuroFuzzy System; Compensation; Classification

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    Han, M-F., Lin, C-T., & Chang, J. Y. (2010). A Compensatory NeuroFuzzy System with Online Constructing and Parameter Learning. In 2010 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010) ( IEEE International Conference on Systems Man and Cybernetics Conference Proceedings). IEEE. https://doi.org/10.1109/ICSMC.2010.5642019