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arXiv · 1304.7423

On Integrating Fuzzy Knowledge Using a Novel Evolutionary Algorithm

Nafisa Afrin Chowdhury·Murshida Khatun·M. M. A. Hashem
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Abstract

Fuzzy systems may be considered as knowledge-based systems that incorporates human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions. The intent of this study is to present a fuzzy knowledge integration framework using a Novel Evolutionary Strategy (NES), which can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach consists of two phases: fuzzy knowledge encoding and fuzzy knowledge integration. Four application domains, the hepatitis diagnosis, the sugarcane breeding prediction, Iris plants classification, and Tic-tac-toe endgame were used to show the performance ofthe proposed knowledge approach. Results show that the fuzzy knowledge base derived using our approach performs better than Genetic Algorithm based approach.

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Nafisa Afrin Chowdhury, Murshida Khatun, M. M. A. Hashem. 2013-04-28. On Integrating Fuzzy Knowledge Using a Novel Evolutionary Algorithm. https://doi.org/10.1109/iccitechn.2007.4579352

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