A Fast Learning Algorithm For Uninorm-based Fuzzy Neural Networks

dc.creatorLemos A.P.
dc.creatorCaminhas W.
dc.creatorGomide F.
dc.date2012
dc.date2015-06-25T20:27:15Z
dc.date2015-11-26T15:23:57Z
dc.date2015-06-25T20:27:15Z
dc.date2015-11-26T15:23:57Z
dc.date.accessioned2018-03-28T22:32:51Z
dc.date.available2018-03-28T22:32:51Z
dc.identifier9781467323376
dc.identifier2012 Annual Meeting Of The North American Fuzzy Information Processing Society, Nafips 2012. , v. , n. , p. - , 2012.
dc.identifier
dc.identifier10.1109/NAFIPS.2012.6290979
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84867724774&partnerID=40&md5=9502b7d1eb73273a41d619efdc705a76
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/90698
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/90698
dc.identifier2-s2.0-84867724774
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1260569
dc.descriptionThis paper suggests a fast learning algorithm for weighted uninorm-based neural networks. Fuzzy neural networks are models capable to approximate functions with high accuracy and to generate transparent models through extraction of linguistic information from the resulting topology. A fuzzy neural network model based on weighted uninorms has been developed recently. It was shown that this model approximates any continuous real function on a compact subset. In this paper we introduce a fast learning algorithm for this class of fuzzy neural networks based on ideas from extreme learning machine. The algorithm is detailed and computational experiments reported to illustrate the accuracy and time efficiency of the learning approach. The results show that neural fuzzy model is accurate and learning speed is as good as or faster than alternative neural network models. © 2012 IEEE.
dc.description
dc.description
dc.description
dc.description
dc.descriptionMinist. Commun. Inf. Technol. Republic Azerbaijan
dc.descriptionPedrycz, W., Fuzzy Neural Networks and Neurocomputations (1993) Fuzzy Sets and Systems, 56 (1), pp. 1-28. , MAY 25
dc.descriptionCaminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy sets based neural networks: Structure, learning and applications (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157
dc.descriptionGomide, F., Pedrycz, W., (2007) Fuzzy Systems Engineering: Toward Human-Centric Computing, , NJ, USA: Wiley Interscience
dc.descriptionBallini, R., Gomide, F., Learning in recurrent, hybrid neurofuzzy networks (2002) IEEE International Conference on Fuzzy Systems, pp. 785-791
dc.descriptionHell, M., Costa, P., Gomide, F., Participatory learning in power transformers thermal modeling (2008) IEEE Transactions on Power Delivery, 23 (4), pp. 2058-2067. , Oct
dc.descriptionGobi, A.E., Pedrycz, D., Logic minimization as an efficient means of fuzzy structure discovery (2008) IEEE Transactions on Fuzzy Systems, 16 (3), pp. 553-566. , JUN
dc.descriptionPedrycz, W., Logic-based fuzzy neurocomputing with unineurons (2006) IEEE Transactions on Fuzzy Systems, 14 (6), pp. 860-873. , DEC
dc.descriptionPedrycz, W., Hirota, K., Uninorm-based logic neurons as adaptive and interpretable processing constructs (2007) Soft Computing, 11 (1), pp. 41-52. , JAN
dc.descriptionHell, M., Gomide, F., Ballini, R., Costa, P., Uninetworks in time series forecasting (2009) NAFIPS 2009. Annual Meeting of the North American Fuzzy Information Processing Society, 2009, pp. 1-6. , june
dc.descriptionLemos, A., Caminhas, W., Gomide, F., New uninorm-based neuron model and fuzzy neural networks (2010) 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1-6
dc.descriptionLemos, A., Kreinovich, V., Caminhas, W., Gomide, F., Universal approximation with uninorm-based fuzzy neural networks (2011) 2011 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1-6. , march
dc.descriptionYager, R., Rybalov, A., Uninorm aggregation operators (1996) Fuzzy Sets and Systems, 80 (1), pp. 111-120. , MAY 27
dc.descriptionCalvo, T., Baets, B.D., Fodor, J., The functional equations of frank and alsina for uninorms and nullnorms (2001) Fuzzy Sets and Systems, 120 (3), pp. 385-394
dc.descriptionHerrera, F., Lozano, M., Verdegay, J.L., Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis (1998) Artif. Intell. Rev., 12 (4), pp. 265-319
dc.descriptionHuang, G.-B., Zhu, Q.-Y., Siew, C.-K., Extreme learning machine: A new learning scheme of feedforward neural networks (2004) 2004 IEEE International Joint Conference on Neural Networks (IJCNN), 2, pp. 985-990. , july vol.2
dc.descriptionHuang, G.-B., Chen, L., Siew, C.-K., Universal approximation using incremental constructive feedforward networks with random hidden nodes (2006) Neural Networks, IEEE Transactions on, 17 (4), pp. 879-892. , july
dc.descriptionYager, R., Uninorms in fuzzy systems modeling (2001) Fuzzy Sets and Systems, 122 (1), pp. 167-175. , AUG 16
dc.descriptionHuang, G.-B., Siew, C.-K., Extreme learning machine with randomly assigned rbf kernels (2005) International Journal of Information Technology, 11 (1), pp. 16-24
dc.descriptionMontesino-Pouzols, F., Lendasse, A., Evolving fuzzy optimally pruned extreme learning machine for regression problems (2010) Evolving Systems, 1 (1), pp. 43-58. , August
dc.descriptionBartlett, P., The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network (1998) IEEE Transactions on Information Theory, 44 (2), pp. 525-536. , mar
dc.descriptionSerre, D., (2002) Matrices: Theory and Applications, , New York, US: Springer- Verlag
dc.descriptionBox, G.E.P., Jenkins, G., (1990) Time Series Analysis, Forecasting and Control, , Holden-Day, Incorporated
dc.descriptionRiedmiller, M., Braun, H., A direct adaptive method for faster backpropagation learning: The rprop algorithm (1993) IEEE International Conference on Neural Networks, 1993, 1, pp. 586-591
dc.descriptionJang, J., ANFIS - Adaptive-Network-Based Fuzzy Inference System (1993) IEEE Transactions on Systems Man and Cybernetics, 23 (3)
dc.languageen
dc.publisher
dc.relation2012 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2012
dc.rightsfechado
dc.sourceScopus
dc.titleA Fast Learning Algorithm For Uninorm-based Fuzzy Neural Networks
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución