Improving Fuzzy Knowledge Integration with Particle Swarm Optimization

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题名:	Improving Fuzzy Knowledge Integration with Particle Swarm Optimization
作者:	蔡進發;Jeffrey, J.P.Tsai
贡献者:	生物與醫學資訊學系
关键词:	"knowledge integration;fuzzy rule;particle swarm optimization;evolutionary computing;swarm intelligence"
日期:	2010
上传时间:	2012-11-23 09:15:10 (UTC+0)
摘要:	"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 student’s 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."
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
显示于类别:	[生物資訊與醫學工程學系 ] 期刊論文

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