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dc.contributor.authorCha, Deaho
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorZhang, Hong
dc.contributor.authorJeng, Dong-Sheng
dc.contributor.editorGary G. Yen
dc.date.accessioned2018-11-14T12:31:09Z
dc.date.available2018-11-14T12:31:09Z
dc.date.issued2006
dc.date.modified2010-10-27T08:29:37Z
dc.identifier.isbn978-0-7803-9490-2
dc.identifier.issn2161-4393
dc.identifier.doi10.1109/IJCNN.2006.247085
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent456163 bytes
dc.format.extent19421 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglish
dc.language.isoeng
dc.publisher2006 IEEE
dc.publisher.placeVancouver
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameIEEE International Joint Conference on Neural Network
dc.relation.ispartofconferencetitle2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10
dc.relation.ispartofdatefrom2006-07-16
dc.relation.ispartofdateto2006-07-21
dc.relation.ispartoflocationVancouver, CANADA
dc.relation.ispartofpagefrom4577
dc.relation.ispartofpagefrom2 pages
dc.relation.ispartofpageto+
dc.relation.ispartofpageto2 pages
dc.rights.retentionY
dc.subject.fieldofresearchcode291205
dc.subject.fieldofresearchcode280212
dc.titleImprovement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefaction
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Engineering and Built Environment
gro.rights.copyright© 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.
gro.date.issued2006
gro.hasfulltextFull Text
gro.griffith.authorZhang, Hong
gro.griffith.authorJeng, Dong-Sheng


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

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