Hybrid SVMR-GPR for modeling of chaotic time series systems with noise and outliers

Authors:
Jin-Tsong Jeng;Chen-Chia Chuang;Chin-Wang Tao
Affiliations:
Department of Computer Science and Information Engineering, National Formosa University, P.O. Box 6-058, Huwei, Huwei Jen, Yunlin County 632, Taiwan;Department of Electrical Engineering, National Ilan University, 1, Sec. 1, Shen-Lung Rd., I-Lan 260, Taiwan;Department of Electrical Engineering, National Ilan University, 1, Sec. 1, Shen-Lung Rd., I-Lan 260, Taiwan
Venue:
Neurocomputing
Year:
2010

Citing 5
Cited 1

Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Tensor Rank One Discriminant Analysis-A convergent method for discriminative multilinear subspace selection

Neurocomputing
Hybrid approach of selecting hyperparameters of support vector machine for regression

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust support vector regression networks for function approximation with outliers

IEEE Transactions on Neural Networks

Identification of nonlinear systems with outliers using wavelet neural networks based on annealing dynamical learning algorithm

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, the hybrid support vector machines for regression (SVMR) and Gaussian processes for regression (GPR) are proposed to deal with training data set with noise and outliers for the chaotic time series systems. In the proposed approach, there are two-stage strategies and can be a sparse approximation. In stage I, the SVMR approach is used to filter out some large noise and outliers in the training data set. Because the large noises and outliers in the training data set are almost removed, the affection of large noises and outliers is also reduced. That is, the proposed approach can be against the large noise and outliers. Hence, the proposed approach is also a robust approach. After stage I, the rest of the training data set is directly used to train the GPR in stage II. From the simulation results, the performance of the proposed approach is superior to least squares support vector machines regression (LS-SVMR), GPR, weighted LS-SVM and robust support vector regression networks when there are noise and outliers on the chaotic time-series systems.