%0 Generic
%A Kalenatic, Dusko
%A Figueroa García, Juan Carlos
%A López, Cesar Amílcar
%8 2010
%U http://hdl.handle.net/10818/36964
%X 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.
%I International Conference on Intelligent Computing
%K Fuzzy Logic System
%K Mean Square Error Criterion
%K North American Fuzzy Information Processing
%K American Fuzzy Information Processing Society
%K Volatile Time Series
%T A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting
%R 10.1007/978-3-642-14922-1_19
%~ Intellectum