Improving fuzzy knowledge integration with particle swarmoptimization

NCU Institutional Repository > 資訊電機學院 > 資訊工程學系 > 期刊論文 > Item 987654321/51552

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/51552

题名:	Improving fuzzy knowledge integration with particle swarmoptimization
作者:	Huang,AFM;Yang,SJH;Wang,MH;Tsai,JJP
贡献者:	資訊工程學系
关键词:	RULE BASE;SYSTEM;OPTIMIZATION;ALGORITHM;PSO;CLASSIFICATION;ENVIRONMENTS;PERFORMANCE;EXTRACTION;DIAGNOSIS
日期:	2010
上传时间:	2012-03-27 18:56:01 (UTC+8)
出版者:	國立中央大學
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 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. (C) 2010 Elsevier Ltd. All rights reserved.
關聯:	EXPERT SYSTEMS WITH APPLICATIONS
显示于类别:	[資訊工程學系] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	435	检视/开启

在NCUIR中所有的数据项都受到原著作权保护.

社群 sharing

数据加载中.....