Cross entropy error function in neural networks

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dc.contributor.author Nasr, G.E.
dc.contributor.author Badr, E.A.
dc.contributor.author Joun, C.
dc.date.accessioned 2017-12-05T14:09:40Z
dc.date.available 2017-12-05T14:09:40Z
dc.date.copyright 2002 en_US
dc.date.issued 2017-12-05
dc.identifier.isbn 1-57735-141-X en_US
dc.identifier.uri http://hdl.handle.net/10725/6723
dc.description.abstract This paper applies artificial neural networks to forecast gasoline consumption. The ANN is implemented using the cross entropy error function in the training stage. The cross entropy function is proven to accelerate the backpropagation algorithm and to provide good overall network performance with relatively short stagnation periods. To forecast gasoline consumption (GC), the ANN uses previous GC data and its determinants in a training data set. The determinants of gasoline consumption employed in this study are the price (P) and car registration (CR). Two ANNs models are presented. The first model is a univariate model based on past GC values. The second model is a trivariate model based on GC, price and car registration time series. Forecasting performance measures such as mean square errors (MSE) and mean absolute deviations (MAD) are presented for both models. en_US
dc.language.iso en en_US
dc.publisher ACM en_US
dc.title Cross entropy error function in neural networks en_US
dc.type Conference Paper / Proceeding en_US
dc.title.subtitle forecasting gasoline demand 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.identifier.ctation Nasr, G. E., Badr, E. A., & Joun, C. (2002, May). Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand. In FLAIRS Conference (pp. 381-384). en_US
dc.author.email genasr@lau.edu.lb en_US
dc.conference.date May 14 - 16, 2002 en_US
dc.conference.pages 381-384 en_US
dc.conference.place Pensacola Beach, Florida en_US
dc.conference.title Proceedings of the Fifteenth International Florida Artificial Intelligence en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://dl.acm.org/citation.cfm?id=708603 en_US
dc.author.affiliation Lebanese American University en_US

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