A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting

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URI: http://hdl.handle.net/10818/36964
Visitar enlace: https://link.springer.com/chap ...
DOI: 10.1007/978-3-642-14922-1_19
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Author
Kalenatic, Dusko; Figueroa García, Juan Carlos; López, Cesar Amílcar
Date
2010
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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Advanced Intelligent Computing Theories and Applications pp 142-149
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Attribution-NonCommercial-NoDerivatives 4.0 InternationalExcept where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International