Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation

Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation

Abstract

Fuzzy modeling has been widely and successfully applied to solve control problems. Traditional fuzzy modeling requires either complete experts?? knowledge or large data sets to generate rule bases that can fully cover the input domain. Although fuzzy rule interpolation (FRI) relaxes this requirement by approximating rules using their neighboring ones, it is still difficult for some real world applications to obtain sufficient experts?? knowledge and data to generate a reasonable sparse rule base to support FRI. Also, the generated rule bases are usually fixed and ther

Publication

Jie Li, Hubert P. H. Shum, Xin Fu, Graham Sexton and Longzhi Yang,
"Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation",
Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
, 2016
Citation: 19## IEEE CIS Outstanding Student-Paper Travel Grants

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References

BibTeX

@inproceedings{li19experience,
 author={Li, Jie and Shum, Hubert P. H. and Fu, Xin and Sexton, Graham and Yang, Longzhi},
 booktitle={Proceedings of the 2016 IEEE World Congress on Computational Intelligence},
 series={FUZZ-IEEE '16},
 title={Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation},
 year={2016},
 month={Jul},
 pages={102--109},
 numpages={9},
 doi={10.1109/FUZZ-IEEE.2016.7737674},
 publisher={IEEE},
 location={Vancouver, Canada},
}

EndNote/RefMan

TY  - CONF
AU  - Li, Jie
AU  - Shum, Hubert P. H.
AU  - Fu, Xin
AU  - Sexton, Graham
AU  - Yang, Longzhi
T2  - Proceedings of the 2016 IEEE World Congress on Computational Intelligence
TI  - Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation
PY  - 2016
Y1  - Jul 2016
SP  - 102
EP  - 109
DO  - 10.1109/FUZZ-IEEE.2016.7737674
PB  - IEEE
ER  - 

Plain Text

Jie Li, Hubert P. H. Shum, Xin Fu, Graham Sexton and Longzhi Yang, "Experience-based Rule Base Generation and Adaptation for Fuzzy Interpolation," in FUZZ-IEEE '16: Proceedings of the 2016 IEEE World Congress on Computational Intelligence, pp. 102-109, Vancouver, Canada, IEEE, Jul 2016.

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