TSK Inference with Sparse Rule Bases

Jie Li, Yanpeng Qu, Hubert P. H. Shum and Longzhi Yang
Proceedings of the 2016 UK Workshop on Computational Intelligence (UKCI), 2016

Citation: 23## Best Paper Award

TSK Inference with Sparse Rule Bases
## Citation counts from Google Scholar as of 2022

Abstract

The Mamdani and TSK fuzzy models are fuzzy inference engines which have been most widely applied in real-world problems. Compared to the Mamdani approach, the TSK approach is more convenient when the crisp outputs are required. Common to both approaches, when a given observation does not overlap with any rule antecedent in the rule base (which usually termed as a sparse rule base), no rule can be fired, and thus no result can be generated. Fuzzy rule interpolation was proposed to address such issue. Although a number of important fuzzy rule interpolation approaches have been proposed in the literature, all of them were developed for Mamdani inference approach, which leads to the fuzzy outputs. This paper extends the traditional TSK fuzzy inference approach to allow inferences on sparse TSK fuzzy rule bases with crisp outputs directly generated. This extension firstly calculates the similarity degrees between a given observation and every individual rule in the rule base, such that the similarity degrees between the observation and all rule antecedents are greater than 0 even when they do not overlap. Then the TSK fuzzy model is extended using the generated matching degrees to derive crisp inference results. The experimentation shows the promising of the approach in enhancing the TSK inference engine when the knowledge represented in the rule base is not complete.

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BibTeX

@inproceedings{li17tsk,
 author={Li, Jie and Qu, Yanpeng and Shum, Hubert P. H. and Yang, Longzhi},
 booktitle={Proceedings of the 2016 UK Workshop on Computational Intelligence},
 series={UKCI '16},
 title={TSK Inference with Sparse Rule Bases},
 year={2016},
 month={Sep},
 pages={107--123},
 numpages={7},
 doi={10.1007/978-3-319-46562-3_8},
 isbn={978-3-319-46562-3},
 publisher={Springer International Publishing},
 Address={Cham},
 location={Lancaster, UK},
}

RIS

TY  - CONF
AU  - Li, Jie
AU  - Qu, Yanpeng
AU  - Shum, Hubert P. H.
AU  - Yang, Longzhi
T2  - Proceedings of the 2016 UK Workshop on Computational Intelligence
TI  - TSK Inference with Sparse Rule Bases
PY  - 2016
Y1  - Sep 2016
SP  - 107
EP  - 123
DO  - 10.1007/978-3-319-46562-3_8
SN  - 978-3-319-46562-3
PB  - Springer International Publishing
ER  - 

Plain Text

Jie Li, Yanpeng Qu, Hubert P. H. Shum and Longzhi Yang, "TSK Inference with Sparse Rule Bases," in UKCI '16: Proceedings of the 2016 UK Workshop on Computational Intelligence, pp. 107-123, Lancaster, UK, Springer International Publishing, Sep 2016.

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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", Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016
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
Jie Li, Longzhi Yang, Hubert P. H. Shum, Graham Sexton and Yao Tan, "Intelligent Home Heating Controller Using Fuzzy Rule Interpolation", Proceedings of the 2015 UK Workshop on Computational Intelligence (UKCI), 2015

 

 
 

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