標題: Integrating membership functions and fuzzy rule sets from multiple knowledge sources
作者: Wang, CH
Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: expert systems;fuzzy machine learning;fuzzy sets;knowledge acquisition;membership functions;operators
公開日期: 16-五月-2000
摘要: Ln this paper, we propose a GA-based fuzzy knowledge-integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed two-phase approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. The combined strings form an initial knowledge population which is then ready for integration. In the knowledge-integration phase. a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge-integration approach. Results show that the fuzzy knowledge-base resulting from using our approach performs better than every individual knowledge base. (C) 2000 Elsevier Science B.V. All rights reserved.
URI: http://hdl.handle.net/11536/30515
ISSN: 0165-0114
期刊: FUZZY SETS AND SYSTEMS
Volume: 112
Issue: 1
起始頁: 141
結束頁: 154
顯示於類別:期刊論文


文件中的檔案:

  1. 000086088800010.pdf