ARFNNs with SVR for prediction of chaotic time series with outliers

Yuyi Fu,Chiaju Wu,Chianan Ko,Jintsong Jeng,Lichun Lai

Expert Systems with Applications: An International Journal（2010）

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摘要

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.

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关键词

annealing robust learning algorithm,fuzzy neural network,combination model,different svr,chaotic time series · outliers · support vector regression · annealing robust learning algorithm,initial structure,initial weight,fuzzy inference system,good performance,chaotic time series,annealing robust fuzzy neural,robust learning algorithm,support vector regression,fuzzy neural networks,radial basis function network,neural network,time series

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