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dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorZhang, Hongen_US
dc.contributor.authorJeng, D.en_US
dc.contributor.editorGary G. Yenen_US
dc.date.accessioned2017-05-03T14:30:34Z
dc.date.available2017-05-03T14:30:34Z
dc.date.issued2006en_US
dc.date.modified2010-10-27T08:29:37Z
dc.identifier.doi10.1109/IJCNN.2006.247085en_AU
dc.identifier.urihttp://hdl.handle.net/10072/13342
dc.description.abstractIn the past decade, artificial neural networks (ANNs) have been widely applied to the engineering problems with a complicated system. ANNs are becoming an important alternative option for solving problems in comparison to traditional engineering solutions, which are usually involved in complicated mathematical theories. In this study, we apply an ANN model to the wave-induced seabed liquefaction problem, which is a key issue in the area of coastal and ocean engineering. Furthermore, we adopted an ANN model with preprocessing (MIN-MAX) on difficult training data. This paper demonstrates the capacity of the proposed ANN model using MIN-MAX pre-processing to provide coastal engineers with another effective tool to analyse the stability of seabed sediment.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent456163 bytes
dc.format.extent19421 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisher2006 IEEEen_US
dc.publisher.placeVancouveren_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofconferencename2006 IEEE World Congress on Computational Intelligenceen_US
dc.relation.ispartofconferencetitle2006 IEEE Symposium Series on Computational Intelligenceen_US
dc.relation.ispartofdatefrom2006-07-16en_US
dc.relation.ispartofdateto2006-07-21en_US
dc.relation.ispartoflocationVancouver, Canadaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode291205en_US
dc.subject.fieldofresearchcode280212en_US
dc.titleImprovement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefactionen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_AU
gro.date.issued2006
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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