Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6619
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Mechanical Engineering-
dc.creatorYuan, J-
dc.date.accessioned2014-12-11T08:25:28Z-
dc.date.available2014-12-11T08:25:28Z-
dc.identifier.issn0001-4966-
dc.identifier.urihttp://hdl.handle.net/10397/6619-
dc.language.isoenen_US
dc.publisherAcoustical Society of Americaen_US
dc.rightsCopyright 2008 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America.en_US
dc.rightsThe following article appeared in Yuan, J. (2008). Self-learning active noise control. Journal of the Acoustical Society of America, 124(4), 2078-2084 and may be found at http://scitation.aip.org/content/asa/journal/jasa/124/4/10.1121/1.2968700en_US
dc.subjectAcoustic field measurementen_US
dc.subjectAcoustic fieldsen_US
dc.subjectAcoustic variables controlen_US
dc.subjectAcousticsen_US
dc.subjectActive noise controlen_US
dc.subjectComputer systemsen_US
dc.subjectNeural networksen_US
dc.subjectOnline systemsen_US
dc.subjectOptimizationen_US
dc.subjectSystem stabilityen_US
dc.titleSelf-learning active noise controlen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: J. Yuanen_US
dc.identifier.spage2078-
dc.identifier.epage2084-
dc.identifier.volume124-
dc.identifier.issue4-
dc.identifier.doi10.1121/1.2968700-
dcterms.abstractAn important step for active noise control (ANC)systems to be practical is to develop model independent ANC (MIANC) systems that tolerate parameter variations in sound fields. Reliabilities and stabilities of many MIANC systems depend on results of online system identifications. Parameter errors due to system identifications may threaten closed-loop stabilities of MIANC systems. A self-learning active noise control (SLANC) system is proposed in this study to stabilize and optimize an ANCsystem in case identified parameters are unreliable. The proposed system uses an objective function to check closed-loop stability. If partial or full value of the objective function exceeds a conservatively preset threshold, a stability threat is detected and the SLANC system will stabilize and optimize the controller without using parameters of sound fields. If the reference signal is available, the SLANC system can be combined with a feedforward controller to generate both destructive interference and active damping in sound fields. The self-learning method is simple and stable for many feedback ANCsystems to deal with a worst case discussed in this study.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of the Acoustical Society of America, Oct. 2008, v. 124, no. 4, 2078-2084-
dcterms.isPartOfJournal of the Acoustical Society of America-
dcterms.issued2008-10-
dc.identifier.isiWOS:000260298600020-
dc.identifier.scopus2-s2.0-53949086336-
dc.identifier.pmid19062848-
dc.identifier.eissn1520-8524-
dc.identifier.rosgroupidr40799-
dc.description.ros2008-2009 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Yuan_Self-learning_Active_Noise.pdf178.17 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

133
Last Week
2
Last month
Citations as of Mar 24, 2024

Downloads

247
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

6
Last Week
0
Last month
0
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

297
Last Week
3
Last month
0
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


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