Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation

Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation

Abstract

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.

Publication

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)

Citation=19## IEEE CIS Outstanding Student-Paper Travel Grants

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References

BibTeX

@inproceedings{tan16towards,
 author={Tan, Yao and Li, Jie and Wonders, Martin and Chao, Fei and Shum, Hubert P. H. and Yang, Longzhi},
 booktitle={Proceedings of the 2016 IEEE World Congress on Computational Intelligence},
 series={FUZZ-IEEE '16},
 title={Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation},
 year={2016},
 month={Jul},
 pages={110--117},
 numpages={8},
 doi={10.1109/FUZZ-IEEE.2016.7737675},
 publisher={IEEE},
 location={Vancouver, Canada},
}

EndNote/RefMan

TY  - CONF
AU  - Tan, Yao
AU  - Li, Jie
AU  - Wonders, Martin
AU  - Chao, Fei
AU  - Shum, Hubert P. H.
AU  - Yang, Longzhi
T2  - Proceedings of the 2016 IEEE World Congress on Computational Intelligence
TI  - Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation
PY  - 2016
Y1  - Jul 2016
SP  - 110
EP  - 117
DO  - 10.1109/FUZZ-IEEE.2016.7737675
PB  - IEEE
ER  - 

Plain Text

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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Last update: 18/06/2019