Hybrid support vector machines learning for fuzzy neural networks with outliers

Abstract

In this study, the hybrid support vector machines for regression (HSVMR) is proposed to deal with training data set with outliers for fuzzy neural networks (FNNs). There are two-stage strategies in 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. Due to the outliers in the training data set are removed, the concept of robust statistic theory have no need to reduce the outlierpsilas 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 H. 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.