File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Improving fuzzy knowledge integration with particle swarmoptimization

TitleImproving fuzzy knowledge integration with particle swarmoptimization
Authors
KeywordsEvolutionary computing
Fuzzy rule
Knowledge integration
Particle swarm optimization
Swarm intelligence
Issue Date2010
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa
Citation
Expert Systems With Applications, 2010, v. 37 n. 12, p. 8770-8783 How to Cite?
AbstractThis 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. © 2010 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/129411
ISSN
2015 Impact Factor: 2.981
2015 SCImago Journal Rankings: 1.839
ISI Accession Number ID
Funding AgencyGrant Number
National Science Council, TaiwanNSC95-2520-S-008-006-MY3
NSC96-2628-S-008-008-MY3
Funding Information:

The authors would like to thank Mr. Evan Y.F. Lee for his assistance in system implementation. This work is supported by National Science Council, Taiwan under the grants NSC95-2520-S-008-006-MY3 and NSC96-2628-S-008-008-MY3.

References

 

DC FieldValueLanguage
dc.contributor.authorHuang, AFMen_HK
dc.contributor.authorYang, SJHen_HK
dc.contributor.authorWang, Men_HK
dc.contributor.authorTsai, JJPen_HK
dc.date.accessioned2010-12-23T08:36:54Z-
dc.date.available2010-12-23T08:36:54Z-
dc.date.issued2010en_HK
dc.identifier.citationExpert Systems With Applications, 2010, v. 37 n. 12, p. 8770-8783en_HK
dc.identifier.issn0957-4174en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129411-
dc.description.abstractThis 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. © 2010 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswaen_HK
dc.relation.ispartofExpert Systems with Applicationsen_HK
dc.subjectEvolutionary computingen_HK
dc.subjectFuzzy ruleen_HK
dc.subjectKnowledge integrationen_HK
dc.subjectParticle swarm optimizationen_HK
dc.subjectSwarm intelligenceen_HK
dc.titleImproving fuzzy knowledge integration with particle swarmoptimizationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0957-4174&volume=37&issue=12&spage=8770&epage=8783&date=2010&atitle=Improving+fuzzy+knowledge+integration+with+particle+swarmoptimization-
dc.identifier.emailWang, M: magwang@hku.hken_HK
dc.identifier.authorityWang, M=rp00967en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.eswa.2010.06.030en_HK
dc.identifier.scopuseid_2-s2.0-77957833395en_HK
dc.identifier.hkuros177149en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77957833395&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue12en_HK
dc.identifier.spage8770en_HK
dc.identifier.epage8783en_HK
dc.identifier.isiWOS:000281339900158-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridHuang, AFM=25926442200en_HK
dc.identifier.scopusauthoridYang, SJH=25924575100en_HK
dc.identifier.scopusauthoridWang, M=8723779700en_HK
dc.identifier.scopusauthoridTsai, JJP=7403610505en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats