Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/6619
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Mechanical Engineering | - |
dc.creator | Yuan, J | - |
dc.date.accessioned | 2014-12-11T08:25:28Z | - |
dc.date.available | 2014-12-11T08:25:28Z | - |
dc.identifier.issn | 0001-4966 | - |
dc.identifier.uri | http://hdl.handle.net/10397/6619 | - |
dc.language.iso | en | en_US |
dc.publisher | Acoustical Society of America | en_US |
dc.rights | Copyright 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.rights | The 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/124/4/10.1121/1.2968700 | en_US |
dc.subject | Acoustic field measurement | en_US |
dc.subject | Acoustic fields | en_US |
dc.subject | Acoustic variables control | en_US |
dc.subject | Acoustics | en_US |
dc.subject | Active noise control | en_US |
dc.subject | Computer systems | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Online systems | en_US |
dc.subject | Optimization | en_US |
dc.subject | System stability | en_US |
dc.title | Self-learning active noise control | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Author name used in this publication: J. Yuan | en_US |
dc.identifier.spage | 2078 | - |
dc.identifier.epage | 2084 | - |
dc.identifier.volume | 124 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1121/1.2968700 | - |
dcterms.abstract | An 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of the Acoustical Society of America, Oct. 2008, v. 124, no. 4, 2078-2084 | - |
dcterms.isPartOf | Journal of the Acoustical Society of America | - |
dcterms.issued | 2008-10 | - |
dc.identifier.isi | WOS:000260298600020 | - |
dc.identifier.scopus | 2-s2.0-53949086336 | - |
dc.identifier.pmid | 19062848 | - |
dc.identifier.eissn | 1520-8524 | - |
dc.identifier.rosgroupid | r40799 | - |
dc.description.ros | 2008-2009 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Yuan_Self-learning_Active_Noise.pdf | 178.17 kB | Adobe PDF | View/Open |
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