ARFNNs with SVR for prediction of chaotic time series with outliers

作者：Fu Yu Yi; Wu Chia Ju; Jeng Jin Tsong^*; Ko Chia Nan

来源：Expert Systems with Applications, 2010, 37(6): 4441-4451.

DOI：10.1016/j.eswa.2009.12.067

• Chaotic time series
• Fuzzy neural networks
• Support vector regression
• Annealing robust learning algorithm

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.

• 出版日期2010-6
• 单位南开大学