Improvement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefaction

File Size Format
40616.pdf 445Kb Adobe PDF View
Title Improvement of an Artificial Neural Network Model using Min-Max Preprocessing for the Prediction of Wave-induced Seabed Liquefaction
Author Cha, Deaho Fred; Blumenstein, Michael Myer; Zhang, Hong; Jeng, D. S.
Publication Title 2006 IEEE Symposium Series on Computational Intelligence
Editor Gary G. Yen
Year Published 2006
Place of publication Vancouver
Publisher 2006 IEEE
Abstract In 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.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/IJCNN.2006.247085
Copyright Statement 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.
ISBN 0-780394895
Conference name 2006 IEEE World Congress on Computational Intelligence
Location Vancouver, Canada
Date From 2006-07-16
Date To 2006-07-21
URI http://hdl.handle.net/10072/13342
Date Accessioned 2007-03-08
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Neural Networks, Genetic Alogrithms and Fuzzy Logic; PRE2009-Ocean Engineering
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

Show simple item record

Griffith University copyright notice