ARFNNs with SVR for prediction of chaotic time series with outliers

Authors:
Yu-Yi Fu;Chia-Ju Wu;Jin-Tsong Jeng;Chia-Nan Ko
Affiliations:
Department of Automation Engineering, Nan Kai University of Technology, Tsaotun, Nantou 542, Taiwan;Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Yunlin 640, Taiwan;Department of Computer Science and Information Engineering, National Formosa University, Huwei, Yunlin 632, Taiwan;Department of Automation Engineering, Nan Kai University of Technology, Tsaotun, Nantou 542, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

This paper demonstrates an approach to predict the chaotic time series with outliers using annealing robust fuzzy neural networks (ARFNNs). A combination model that merges support vector regression (SVR), radial basis function networks (RBFNs) and simplified fuzzy inference system is used. The SVR has the good performances to determine the number of rules in the simplified fuzzy inference system and initial weights for the fuzzy neural networks (FNNs). Based on these initial structures, and then annealing robust learning algorithm (ARLA) can be used effectively to overcome outliers and adjust the parameters of structures. Simulation results show the superiority of the proposed method with different SVR for training and prediction of chaotic time series with outliers.