Two-Stages Support Vector Regression for Fuzzy Neural Networks with Outliers

10.30000/IJFS.200903.0003

Chen-Chia Chuang；Jin-Tsong Jeng；C.W. Tao

Outliers ； Support vector machines for regression ； Least squares support vector machines for regression ； Fuzzy neural network

International Journal of Fuzzy Systems

11卷1期（2009 / 03 / 01）

20 - 28

英文

In this study, two-stages support vector regression (TSSVR) approach is proposed to deal with training data set with outliers for fuzzy neural networks (FNNs). The proposed approach in the stage Ⅰ, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set and determine the number of fuzzy rule. Due to the outliers in the training data set are removed, the concept of robust statistic theory have no need to reduce the outlier's effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the sparse least squares support vector machines for regression (LS-SVMR) in the stage Ⅱ. Consequently, the learning mechanism of the proposed approach for fuzzy neural network does not need iterated learning for simplified fuzzy inference systems. Based on the simulation results, the performance of the proposed approach is superior to the robust LS-SVMR approach when the outliers are existed.