Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation

Curvature-Based 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 are only applicable to problems with dense rule bases covering the entire problem domains, whilst fuzzy rule interpolation (FRI) works with sparse rule bases that do not cover certain inputs. Thanks to its ability to work with a rule base with less number of rules, FRI approaches have been utilised as a means to reduce system complexity for complex fuzzy models. This is implemented by removing the rules that can be approximated by their neighbours. Most of the existing fuzzy rule base generation and simplification approaches only target dense rule bases for traditional fuzzy inference systems. This paper proposes a new sparse fuzzy rule base generation method to support FRI. In particular, this approach uses curvature values to identify important rules that cannot be accurately approximated by their neighbouring ones for initialising a compact rule base. The initialised rule base is then optimised using an optimisation algorithm by fine-tuning the membership functions of the involved fuzzy sets. Experiments with a simulation model and a real-world application demonstrate the working principle and the actual performance of the proposed system, with results comparable to the traditional methods using rule bases with more rules.

Publication

Yao Tan, Hubert P. H. Shum, Fei Chao, Vaidehi Vijayakumar and Longzhi Yang,
"Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation",
Journal of Intelligent & Fuzzy Systems (JIFS)

Citation=3##

## Citation count from Google Scholar as of 2018

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References

BibTeX

@article{yao19curvature,
 author={Tan, Yao and Shum, Hubert P. H. and Chao, Fei and Vijayakumar, Vaidehi and Yang, Longzhi},
 journal={Journal of Intelligent & Fuzzy Systems},
 title={Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation},
 year={2019},
 numpages={12},
 doi={10.3233/JIFS-169978},
 publisher={IOS Press},
}

EndNote/RefMan

TY  - JOUR
AU  - Tan, Yao
AU  - Shum, Hubert P. H.
AU  - Chao, Fei
AU  - Vijayakumar, Vaidehi
AU  - Yang, Longzhi
T2  - Journal of Intelligent & Fuzzy Systems
TI  - Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation
PY  - 2019
DO  - 10.3233/JIFS-169978
PB  - IOS Press
ER  - 

Plain Text

Yao Tan, Hubert P. H. Shum, Fei Chao, Vaidehi Vijayakumar and Longzhi Yang, "Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation," Journal of Intelligent & Fuzzy Systems, IOS Press, 2019.

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Last update: 18/06/2019