Abstract
This paper presents an hybrid Neuro-Evolutive algorithm for a First-order Interval Type-2 TSK Fuzzy Logic System applied to a volatile weather forecasting case. All results are tested by statistical tests as Goldfeld-Quant, Ljung-Box, ARCH, Runs, Turning Points, Bayesian, Akaike and Hannan-Quin criteria. Some methodological aspects about a hybrid implementation among ANFIS, an Evolutive Optimizer and a First order Interval Type-2 TSK FLS are presented. The selected type-reduction algorithm is the IASCO algorithm proposed by Melgarejo in [1] since it presents better computing properties than other algorithms.
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References
Melgarejo, M., Bernal, H., Duran, K.: Improved iterative algorithm for computing the generalized centroid of an interval type-2 fuzzy set. In: 2008 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), vol. 27, pp. 1–6. IEEE, Los Alamitos (2008)
Mendez, G., Castillo, O.: Interval type-2 tsk fuzzy logic systems using hybrid learning algorithm. In: Proceedings of IEEE FUZZ Conference, vol. 27, pp. 230–235. IEEE, Los Alamitos (2005)
Figueroa, J.C.: An evolutive interval type-2 TSK fuzzy logic system for volatile time series identification. In: 2009 Conference on Systems, Man and Cybernetics, pp. 1–6. IEEE, Los Alamitos (2009)
Figueroa, J.C., Soriano, J.J.: A comparison of ANFIS, ANN and DBR systems on volatile time series identification. In: IEEE (ed.) 2007 Annual Meeting of the North American Fuzzy Information Processing Society, vol. 26, pp. 321–326. IEEE, Los Alamitos (2007)
Goldfeld, S., Quandt, R.: Nonlinear Methods in Econometrics. North Holland, Amsterdam (1972)
Brockwell, P., Davis, R.: Time Series: Theory and Methods. Springer, Heidelberg (1998)
Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden Day Publishing (1970)
Mendel, J.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall, Englewood Cliffs (1994)
Ljung, G.M., Box, G.E.P.: Information theory and the extension of the maximum likelihood principle. Biometrika 65, 553–564 (1978)
Hannan, E.J.: The estimation of the order of an arma process. Annals of Statistics 8, 1071–1081 (1981)
Ljung, G.M., Box, G.E.P.: On a measure of lack of fit in time series models. Biometrika 65, 297 (1978)
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Kalenatic, D., Figueroa-GarcĂa, J.C., Lopez, C.A. (2010). A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_19
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DOI: https://doi.org/10.1007/978-3-642-14922-1_19
Publisher Name: Springer, Berlin, Heidelberg
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