A neuro-evolutive interval type-2 TSK fuzzy system for volatile weather forecasting

Authors:
Dusko Kalenatic;Juan C. Figueroa-García;Cesar Amilcar Lopez
Affiliations:
Universidad de La Sabana, Chia, Colombia;Universidad de La Sabana, Chia, Colombia;Universidad de La Sabana, Chia, Colombia
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

Citing 2
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Time series: theory and methods

Time series: theory and methods
An evolutive interval type-2 TSK fuzzy logic system for volatile time series identification

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

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Abstract

This paper presents an hybrid Neuro-Evolutive algorithm for a Firstorder Interval Type-2 TSK Fuzzy Logic System applied to a volatile weather forecasting case. All results are tested by statistical tests asGoldfeld-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.