Abstract
This paper presents a training algorithm for regularized fuzzy neural networks which is able to generate consistent and accurate models while adding some level of interpretation to applied problems to act in the prediction of time series. Learning is achieved through extreme learning machines to estimate the parameters and a technique of selection of characteristics using regularization concept and resampling, which is able to perform the definition of the network topology through the selection of subsets of fuzzy neuron more significant to the problem. Numerical experiments are presented for time series problems using benchmark bases on machine learning problems. The results obtained are compared to other techniques of prediction of reference series in the literature. The model made rough estimates of the responses obtained by the models of fuzzy neural networks for time series forecasting with fewer fuzzy rules.
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References
Angelov, P.P., Filev, D.P.: An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 484–498 (2004)
Bach, F.R.: Bolasso: model consistent lasso estimation through the bootstrap. In: Proceedings of the 25th International Conference on Machine learning, pp. 33–40. ACM, July 2008
Bordignon, F.L.: Aprendizado extremo para redes neurais fuzzy baseadas em uninormas (2013)
Bordignon, F., Gomide, F.: Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127, 13–20 (2014)
Box, G.E., Box, G.M.J., Gregory, C.R.: Time series analysis: forecasting and control, No. 04, QA280, B6 1994 (1994)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Stat. 32(2), 407–499 (2004)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. vol. 2, no. 1. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Hell, M.B.: Abordagem neurofuzzy para modelagem de sistemas dinamicos não lineares. Doctoral dissertation, Faculdade de Engenharia Elétrica e de Computação, Universidade Estadual de Campinas (UNICAMP) (2008)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Kasabov, N.K., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10(2), 144–154 (2002)
Leite, D., Gomide, F., Ballini, R., Costa, P.: Fuzzy granular evolving modeling for time series prediction. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 2794–2801. IEEE, June 2011
Lemos, A., Caminhas, W., Gomide, F.: New uninorm-based neuron model and fuzzy neural networks. In: 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6. IEEE, July 2010
Lemos, A.P., Caminhas, W., Gomide, F.: A fast learning algorithm for uninorm-based fuzzy neural networks. In: 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6. IEEE, August 2012
Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press, Cambridge (2012)
Pedrycz, W.: Fuzzy neural networks and neurocomputations. Fuzzy Sets Syst. 56(1), 1–28 (1993)
Rosa, R., Gomide, F., Ballini, R.: Evolving hybrid neural fuzzy network for system modeling and time series forecasting. In: 2013 12th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 378–383. IEEE, December 2013
Souza, P.V.C., Lemos, A.P.: Redes Neurais Nebulosas para problemas de Classificação. In: Simpósio Brasileiro de Automação Inteligente, Natal-RN. XII SBAI-Simpósio Brasileiro de Automação Inteligente (2015)
Yager, R.R., Rybalov, A.: Uninorm aggregation operators. Fuzzy Sets Syst. 80(1), 111–120 (1996)
Souza, P.V.C.: Regularized fuzzy neural networks for pattern classification problems. Int. J. Appl. Eng. Res. 13(5), 2985–2991 (2018)
Dash, P.K., Ramakrishna, G., Liew, A.C., Rahman, S.: Fuzzy neural networks for time-series forecasting of electric load. IEE Proc.-Gener. Transm. Distrib. 142(5), 535–544 (1995)
Maguire, L.P., Roche, B., McGinnity, T.M., McDaid, L.J.: Predicting a chaotic time series using a fuzzy neural network. Inf. Sci. 112(1–4), 125–136 (1998)
Braga, A.D.P., Carvalho, A.P.L.F., Ludermir, T.B.: Redes neurais artificiais: teoria e aplicações, pp. 5–55. Livros Técnicos e CientÃficos (2000)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
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de Campos Souza, P.V., Torres, L.C.B. (2018). Regularized Fuzzy Neural Network Based on Or Neuron for Time Series Forecasting. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_2
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