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INSTITUTO NACIONAL DE ASTROFÍSICA, ÓPTICA Y eLECTRÓNICA |
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2014. Fonseca R, Gómez-Gil P. Temporal Validated Meta-Learning for Long-Term Forecasting of Chaotic Time Series Using Monte Carlo Cross-Validation. Recent Advances on Hybrid Approaches for Designing. Intelligent Systems, Studies in Computational Intelligence 547, O Castillo et al. (eds.), Springer International Publishing Switzerland 2014. DOI: 10.1007/978-3-319-05170-3_24. 2014. Fonseca-Delgado, R, Gomez-Gil, P. “Selecting and Combining Models with Self-Organizing Maps for Long-Term Forecasting of Chaotic Time Series.” Proceedings of the 2014 International Joint Conference on Neural Network (IJCNN), part of the 2014 IEEE World Congress on Computational Intelligence, July 6-11, Beijing China, pp. 2616-2623. DOI: 10.1109/IJCNN.2014.6889454 2014. V. Alarcon-Aquino, J. M. Ramirez-Cortes, P. Gomez-Gil, O. Starostenko, Y. Garcia-Gonzalez. “Network Intrusion Detection Using Self-Recurrent Wavelet Neural Network with Multidimensional Radial Wavelons” Information Technology and Control. Vol. 43, No. 4, pp.347-358. DOI: http://dx.doi.org/10.5755/j01.itc.43.4.4626 Print ISNN: 1392-124X, JCR FI=0.813. 2014. Rigoberto Fonseca-Delgado, María del Pilar Gómez-Gil. Reporte Técnico No. CCC-14-003. “Meta-aprendizaje Auto-organizado Temporal para Predicción a Largo Plazo de Series de Tiempo Caóticas. 14 de octubre de 2014. Instituto Nacional de Astrofísica, Óptica y Electrónica. Coordinación de Ciencias de la Computación, Tonantzintla, Puebla. México. 2013. Fonseca-Delgado R, Gomez-Gil, P. “An assessment of ten-fold and Monte Carlo cross validations for time series forecasting.” Proceedings of the 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control CCE 2013) Sept. 30 – Oct. 6, 2013. DOI : 10.1109/ICEEE.2013.6676075 2013. Fonseca R, Gómez-Gil P “Temporal self-organized meta-learning for predicting chaotic time series,” Special Issue: Advances in Pattern Recognition, Volume Editors: J.A. Olvera-López, José Francisco Martínez Trinidad, Jesús Ariel Carrasco Ochoa, J. Salas Rodríguez, Gabriella Sanniti di Baja, Research in Computing Science, Vol 61, ISSN: 1870-4069, 2013. Pp 13-22. http://www.micai.org/rcs/ 2011. Gómez-Gil P, Ramírez-Cortés JM, Pomares Hernández SE, Alarcón-Aquino V. “A Neural Network Scheme for Long-term Forecasting of Chaotic Time Series” Neural Processing Letters, Vol. 33, No. 3, June 2011, pp. 215-233. ISSN:1370-4621 Published online: March 8, 2011. DOI: 10.1007/s11063-011-9174-0 (cited at JCR Science Edition—2009) (preliminary PDF) 2010. Alarcón-Aquino V, Starostenko O, Ramírez-Cortés JM, Gómez-Gil P, García-Treviño ES. “Initialisation and training procedures for wavelet networks applied to chaotic time series.” Engineering Intelligent Systems. Vol. 18, No. 1, March 2010, CRL Publishing, ISSN: 1472-8915 (cited at JCR Science Edition- 2009). 2010. Gómez-Gil P, García-Pedrero A and Ramírez-Cortes JM. “Composite Recurrent Neural Networks for Long-Term Prediction of Highly-Dynamic Time Series Supported by Wavelet Decomposition”, Soft Computing for Intelligent Control and Mobile Robotics, Vol. 318/2011, pp.253-268, Castillo O, Janusz K and Pedrycz W. Editors, Springer-Verlag. DOI:10.1007/978-3-642-15534-5_16. 2010. García-Pedrero A, Gómez-Gil P. “Time Series Forecasting using Recurrent Neural Networks and Wavelet Reconstructed Signals”. Proceedings of the 20th. International Conference on Electronics, Communications and Computers. CONIELECOMP 2010. Puebla, Pue. DOI: 10.1109/CONIELECOMP.2010.5440775 2009. Bernardo-Torres A, Gómez-Gil, P. “One-step Forecasting on Seismograms using multi-layer Perceptrons”. Proceedings of the 2009 6th. International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (formerly known as ICEEE). November 2009. 2007. Gómez-Gil, P. “Long Term Prediction, Chaos and Artificial Neural Networks. Where is the meeting point?” Engineering Letters. Vo. 15, Number 1. August 2007. ISSN: 1816-0948 (online version), 1816-093X (printed version). 2006. Gómez-Gil P, Ramirez-Cortes M. ”Experiments with a Hybrid-Complex Neural Networks for Long Term Prediction of Electrocardiograms.” Proceedings of the IEEE 2006 International World Congress of Computational Intelligence, IJCNN 2006”. Vancouver. Canada. July 2006. DOI: 10.1109/IJCNN.2006.246952 2000. Gómez-Gil P, Ramirez-Cortés JM, Oldham WJB. “Efectos de Cambios Estructurales de la Red Neuronal HCNN en el pronóstico de señales caóticas.” Proceedings of the X International Congress of Electronics, Communications and Computers, CONIELECOMP 2000. Universidad de las Américas, Puebla. Puebla, pp. 103-107. March 2000. 1999. Gómez-Gil P, Oldham WJB. “On the use of hybrid neural networks and non linear invariants for prediction of electrocardiograms“ Proceedings of the International Joint Conference of Neural Networks IJCNN 99. Washington July, 10-16. 5, Vol. 5. pp. 3661-3664. DOI: 10.1109/IJCNN.1999.836264 1999. Gómez-Gil P, Ramírez Cortés JM, Baez-López D. “Análisis sobre el efecto de conexiones recurrentes de la red neuronal compleja para predicción de electrocardiogramas.” Memorias del IV simposio Iberoamericano en Reconocimiento de Patrones SIARP’99. La Habana, Cuba. Marzo de 1999. 1998. Gómez-Gil P, Oldham W. “Recurrent Neural Networks as a Tool for Modelling and Prediction of Electrocardiograms.” Proceedings of the World multi-conference on Systemics, Cybernetics and Informatics (SCI’98), Orlando, USA, 1998. 1997. Gómez-Gil P, Oldham W. “Modeling and Prediction of Time Series Using Recurrent Neural Networks: an Application to ECG.” Proceedings of the “Second Joint Mexico-US International Workshop on Neural Networks and Neurocontrol Sian Ka’an ‘97,” Quintana Roo, México, August 1997. Pp. 224 – 228 1997. Gómez-Gil P, Oldham WJB. “Application of Recurrent Neural Networks for the Prediction on the Behavior of Biological Oscillators.” Proceedings of the VII International Conference on Electronics, Communications and Computers. CONIELECOMP 97”, Puebla, México, February 1997. |
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SELECTED PUBLICATIONS |
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Last modified on April 10, 2015 |
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Non-linear time series approximation and forecasting, based on neural systems and multi-resolution analysis |