Improving fuzzy knowledge integration with particle swarmoptimization

Angus F.M. Huang, Stephen J.H. Yang, Minhong Wang, Jeffrey J.P. Tsai

研究成果: 雜誌貢獻期刊論文同行評審

19 引文 斯高帕斯(Scopus)


This paper presents an approach to integrate multiple fuzzy knowledge bases for increasing the accuracy and decreasing the complexity of the integrated knowledge base. The proposed approach consists of two phases: PSO-based fuzzy knowledge encoding, and PSO-based fuzzy knowledge fusion. In the encoding phase, the fuzzy rule sets and fuzzy sets with its corresponding membership functions are encoded as a string and are put together in the initial knowledge population. In the fusion phase, the particle swarm algorithm is used to explore the fuzzy rule sets, fuzzy sets and membership functions to its optimal or the approximately optimal extent. Two application domains, including diagnosis on students' program learning style and situational learning services composition, were used to demonstrate the performance of the proposed knowledge integration approach. Experiment results revealed that our approach will effectively increase the accuracy and decrease the complexity of integrated knowledge base. The results of this study could extend the effectiveness of knowledge inference and decision making.

頁(從 - 到)8770-8783
期刊Expert Systems with Applications
出版狀態已出版 - 12月 2010


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