Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44383
DC FieldValueLanguage
dc.contributor.authorJuang, C.F.en_US
dc.contributor.author莊家峰zh_TW
dc.contributor.authorHuang, S.T.en_US
dc.contributor.authorDuh, F.B.en_US
dc.date2006zh_TW
dc.date.accessioned2014-06-06T08:12:13Z-
dc.date.available2014-06-06T08:12:13Z-
dc.identifier.issn0925-2312zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/44383-
dc.description.abstractPractical mold temperature control of a rubber injection-molding machine is studied in this paper. The controller used is a recurrent fuzzy network called Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN), which is characterized by its recurrent structure, on-line structure and parameter learning. Due to the powerful learning ability of TRFN, a simple controller design scheme using direct inverse configuration is proposed. With recurrent structure in TRFN, no a priori knowledge of the molding machine order is required. The designed TRFN controller performs well even if the sampling interval is different from the original one used for training. The design of TRFN consists of off-line and oil-line training. For off-line learning, structure and parameter of TRFN are learned, and the consequent part parameters are tuned by Kalman filter algorithm. On-line learning is performed to fine tune the consequent parameters of TRFN and achieve a better control performance with the use a simple gradient descent algorithm. Practical experiments and comparisons with other types of controllers demonstrate the performance of the proposed TRFN controller. (c) 2006 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USzh_TW
dc.relationNeurocomputingen_US
dc.relation.ispartofseriesNeurocomputing, Volume 70, Issue 1-3, Page(s) 559-567.en_US
dc.relation.urihttp://dx.doi.org/10.1016/j.neucom.2005.11.003en_US
dc.subjectrecurrent fuzzy networksen_US
dc.subjectneural fuzzy networksen_US
dc.subjectfuzzy controlen_US
dc.subjectdirecten_US
dc.subjectinverse controlen_US
dc.subjecttemperature controlen_US
dc.subjectgeneralized predictive controlen_US
dc.subjectinference networken_US
dc.subjectsystemsen_US
dc.subjectidentificationen_US
dc.titleMold temperature control of a rubber injection-molding machine by TSK-type recurrent neural fuzzy networken_US
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1016/j.neucom.2005.11.003zh_TW
item.grantfulltextnone-
item.openairetypeJournal Article-
item.languageiso639-1en_US-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextno fulltext-
Appears in Collections:電機工程學系所
Show simple item record
 

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.