完整後設資料紀錄
DC 欄位語言
dc.contributor.authorWang, CHen_US
dc.contributor.authorHong, TPen_US
dc.contributor.authorTseng, SSen_US
dc.date.accessioned2014-12-08T15:45:16Z-
dc.date.available2014-12-08T15:45:16Z-
dc.date.issued2000-05-16en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://hdl.handle.net/11536/30515-
dc.description.abstractLn 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.en_US
dc.language.isoen_USen_US
dc.subjectexpert systemsen_US
dc.subjectfuzzy machine learningen_US
dc.subjectfuzzy setsen_US
dc.subjectknowledge acquisitionen_US
dc.subjectmembership functionsen_US
dc.subjectoperatorsen_US
dc.titleIntegrating membership functions and fuzzy rule sets from multiple knowledge sourcesen_US
dc.typeArticleen_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume112en_US
dc.citation.issue1en_US
dc.citation.spage141en_US
dc.citation.epage154en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000086088800010-
dc.citation.woscount39-
顯示於類別:期刊論文


文件中的檔案:

  1. 000086088800010.pdf

若為 zip 檔案,請下載檔案解壓縮後,用瀏覽器開啟資料夾中的 index.html 瀏覽全文。