Fuzzy inference systems have been successfully applied to many real-world applications. Traditional fuzzy inference systems only applicable to problems with dense rule bases by which any observation can be covered; while fuzzy rule interpolation is also able to work with sparse rule bases which may not cover certain observations. Thanks to its ability to work with less 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 firstly identifies those important rules which 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 involved fuzzy sets. Digital simulated scenario is employed to demonstrate the working of the proposed system, with promising results generated.
Yao Tan, Jie Li, Martin Wonders, Fei Chao, Hubert P. H. Shum and Longzhi Yang,
"Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation",
Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016
Citation: 27## IEEE CIS Outstanding Student-Paper Travel Grants
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Yao Tan, Jie Li, Martin Wonders, Fei Chao, Hubert P. H. Shum and Longzhi Yang, "Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation," in FUZZ-IEEE '16: Proceedings of the 2016 IEEE World Congress on Computational Intelligence, pp. 110-117, Vancouver, Canada, IEEE, Jul 2016.
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