Mostrar el registro sencillo del ítem

dc.contributor.authorSánchez-Sánchez, Paola A.
dc.contributor.authorGarcía-González, José Rafael
dc.description.abstractArtificial Neural Networks (ANN) consists of some components, such as architecture and learning algorithm. These components have a significant effect on the performance of the ANN, but finding good parameters is a difficult task to achieve. An important requirement for this task is to ensure the reduction of error when inputs and/or hidden neurons are added. In practice, it is assumed that this requirement is always true, but usually it is false. In this paper, we propose a new algorithm that ensures error decrease when input variables and/or hidden neurons are added to the neural network. The behavior of two traditional algorithms and the proposed algorithm in the forecast of Airline time series were compared. The empirical results indicate that the proposed algorithm allows a steady decrease of fit error in all cases, where de most important and differentiable feature is the fact that reach values close to zero, which is not true for the other algorithms. Therefore, it can be used as a suitable alternative algorithm, especially when it needs a good fit.eng
dc.publisherScience publicationsspa
dc.sourceJournal of Computer Scienceeng
dc.sourceVol. 13, No. 7 (2017)spa
dc.source.uriDOI: 10.3844/jcssp.2017.211.217spa
dc.subjectArtificial Neural Networks (ANN)eng
dc.subjectTime Series Forecastingeng
dc.subjectLearning Algorithmseng
dc.subjectError Reductioneng
dc.titleA New Methodology for Neural Network Training Ensures Error Reduction in Time Series Forecastingeng
dcterms.referencesAnastasiadis, A., G. Magoulas and M. Vrahatis, 2003. An efficient improvement of the Rprop algorithm. Proceedings of the 1st International Workshop on Artificial Neural Networks in Pattern Recognition, Sept. 12-13, At Florence, Italy, pp: 197-201.eng
dcterms.referencesCrone, S. and N. Korentzes, 2009. Input-variable specification for neural networks-an analysis of forecasting low and high time series frequency. Proceedings of the International Joint Conference on Neural Networks, Jun. 14-19, IEEE Xplore Press, pp: 3221-3228. DOI: 10.1109/IJCNN.2009.5179046eng
dcterms.referencesFaraway, J. and C. Chatfield, 1998. Time series forecasting with neural networks: A comparative study using the airline data. Applied Stat., 47: 231-250. DOI: 10.1111/1467-9876.00109eng
dcterms.referencesHornik, K., M. Stinchcombe and H. White, 1989. Multilayer feed forward networks are universal approximators. Neural Netw., 2: 359-366. DOI: 10.1016/0893-6080(89)90020-8eng
dcterms.referencesIgel, C. and M. Husken, 2003. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing, 50: 105-123. DOI: 10.1016/S0925-2312(01)00700-7eng
dcterms.referencesMurata, N., S. Yoshizawa and S. Amari, 1994. Network information criterion determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Netw., 5: 865-872. DOI: 10.1109/72.329683eng
dcterms.referencesNam, K. and T. Schaefer, 1995. Forecasting international airline passenger traffic using neural networks. Logist. Transport. Rev., 31: 239-251.eng
dcterms.referencesQi, M. and P. Zhang, 2001. An investigation of model selection criteria for neural network time series forecasting. Eur. J. Operat. Res., 132: 666-680. DOI: 10.1016/S0377-2217(00)00171-5eng
dcterms.referencesSánchez, P. and J.D. Velásquez, 2010. Problemas de investigación en la predicción de series de tiempo con redes neuronales artificiales. Rev. Avances Sistemas Inform., 7: 67-73.eng
dcterms.referencesZhang, G., B. Patuwo and M. Hu, 1998. Forecasting with artificial neural networks: The state of the art. Int. J. Forecast., 14: 35-62. DOI: 10.1016/S0169-2070(97)00044-7eng

Ficheros en el ítem


Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos
    Artículos científicos evaluados por pares

Mostrar el registro sencillo del ítem