Iterative RBF neural networks as metamodels of stochastic simulations

LAUR Repository

Show simple item record

dc.contributor.author Nasr, G.
dc.contributor.author Meghabghab, G.
dc.date.accessioned 2017-12-05T12:50:52Z
dc.date.available 2017-12-05T12:50:52Z
dc.date.issued 2017-12-05
dc.identifier.uri http://hdl.handle.net/10725/6720
dc.description.abstract Research into emerging technological approaches to make computer simulations more effective and efficient is an essential ingredient to developing successful manufacturing models. This study is a premiere study in using neural networks in metamodeling stochastic simulation in manufacturing domain. A new iterative RBF neural network was developed rather than the baseline ANN models which were used in stochastic simulation metamodeling in domains such as combat simulations in the military, service industries, and transportation companies. Given the fact that typical stochastic simulation metamodeling approaches involves the use of regression models in response surface methods, RBF become a natural target for such an attempt because they use a family of surfaces each of which naturally divides an input space into 2 regions and the n patterns will be assigned either class X+ or X-. This dichotomy of the points is said to be separable with respect to the family of surfaces if there exists a surface in the family that separates the points in the class X+ from those in the class X-. In fact, for the evaluation of the quality of a ball steel, RBF metamodel trained on 1521 training examples from a set of 13000 different simulation runs and was able to outperform direct simulation on 120 additional test examples which were not included in the training set. en_US
dc.language.iso en en_US
dc.publisher IEEE Xplore en_US
dc.title Iterative RBF neural networks as metamodels of stochastic simulations en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SOE en_US
dc.author.idnumber 199390170 en_US
dc.author.department Electrical And Computer Engineering en_US
dc.description.embargo N/A en_US
dc.keywords Neural networks en_US
dc.keywords Stochastic processes en_US
dc.keywords Response surface methodology en_US
dc.keywords Metamodeling en_US
dc.keywords Virtual manufacturing en_US
dc.keywords Computer simulation en_US
dc.keywords Computer aided manufacturing en_US
dc.keywords Computational modeling en_US
dc.keywords Artificial neural networks en_US
dc.keywords Defense industry en_US
dc.identifier.ctation Meghabghab, G., & Nasr, G. (1999). Iterative RBF neural networks as metamodels of stochastic simulations. In Intelligent Processing and Manufacturing of Materials, 1999. IPMM'99. Proceedings of the Second International Conference on (Vol. 2, pp. 729-734). IEEE. en_US
dc.author.email genasr@lau.edu.lb en_US
dc.conference.date 10-15 July 1999 en_US
dc.conference.place Honolulu, HI, USA en_US
dc.conference.subtitle 729-734 en_US
dc.conference.title Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99 en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://ieeexplore.ieee.org/abstract/document/791478/ en_US
dc.author.affiliation Lebanese American University en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search LAUR

Advanced Search


My Account