Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection

Keem Siah Yap, Sheng Yuong Wong, Sieh Kiong Tiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

The fuzzy rule sets, which have been derived from the hybrid neural network model, called the O-EGART-PR-FIS, is an integration of the Adaptive Resonance Theory (ART) into Generalized Regression Neural Network (GRNN), display substantial redundancy and low interpretability that leads to time-consuming prediction process. The O-EGART-PR-FIS approach can achieve the highest accuracy rate among all, however the extracted rules are less compact. Hence, in this paper, we propose a genetic algorithm based method with the inclusion of the 'Don't Care' antecedent (hereafter denoted as DC-GA) to the foundation of the O-EGART-PR-FIS, with the aim of further optimizing the existing fuzzy rules. The improved model is applied to two benchmark problems, and the rules extracted are analyzed, discussed and compared with other published methods. From the comparison results, it is observed that the improved model is attested to be statistically superior to other ANN models. Therefore, it reveals the efficacy of DC-GA in eliciting a set of compact and yet easily comprehensible rules while sustaining a high classification performance.

Original languageEnglish
Title of host publicationProceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013
DOIs
Publication statusPublished - 2013
Event2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 - Cagliari, Italy
Duration: 10 Sep 201313 Sep 2013

Other

Other2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013
CountryItaly
CityCagliari
Period10/09/1313/09/13

Fingerprint

Fuzzy rules
Fault detection
Genetic algorithms
Neural networks
Redundancy

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Computer Science Applications

Cite this

Yap, K. S., Wong, S. Y., & Tiong, S. K. (2013). Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection. In Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 [6648106] https://doi.org/10.1109/ETFA.2013.6648106
Yap, Keem Siah ; Wong, Sheng Yuong ; Tiong, Sieh Kiong. / Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection. Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013. 2013.
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Yap, KS, Wong, SY & Tiong, SK 2013, Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection. in Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013., 6648106, 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013, Cagliari, Italy, 10/09/13. https://doi.org/10.1109/ETFA.2013.6648106

Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection. / Yap, Keem Siah; Wong, Sheng Yuong; Tiong, Sieh Kiong.

Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013. 2013. 6648106.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Yap KS, Wong SY, Tiong SK. Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection. In Proceedings of 2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013. 2013. 6648106 https://doi.org/10.1109/ETFA.2013.6648106