Towards sparse rule base generation for fuzzy rule interpolation

Yao Tan, Jie Li, Martin Wonders, Chao Fei, Hubert Shum, Longzhi Yang

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Abstract

Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems are only applicable to problems with dense rule bases by which the entire input domain is fully covered, whilst fuzzy rule interpolation (FRI) is also able to work with sparse rule bases that may not cover certain observations. Thanks to its ability to work with fewer rules, fuzzy rule interpolation approaches have also been utilised to reduce system complexity by removing those rules which can be approximated by their neighbouring ones for complex fuzzy models. A number of important fuzzy rule base generation approaches have been proposed in the literature, but the majority of these only target dense rule bases for traditional fuzzy inference systems. This paper proposes a novel sparse fuzzy rule base generation method to support FRI. The approach first identifies important rules that cannot be accurately approximated by their neighbouring ones to initialise the rule base. Then the raw rule base is optimised by fine-tuning the membership functions of the fuzzy sets. Experimentation is conducted to demonstrate the working principles of the proposed system, with results comparable to those of traditional methods.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Pages110-117
Number of pages8
DOIs
Publication statusPublished - 10 Nov 2016
Event IEEE World Congress on Computational Intelligence 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

Conference IEEE World Congress on Computational Intelligence 2016
Abbreviated titleWCCI 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

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  • Cite this

    Tan, Y., Li, J., Wonders, M., Fei, C., Shum, H., & Yang, L. (2016). Towards sparse rule base generation for fuzzy rule interpolation. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 110-117). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2016.7737675