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Yubing Han
Nanjing University of Science and Technology

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Nonlinear/Non-Gaussian Time Series Prediction Based on RBF-HMM-GMM Model Dongqing Zhang; Yubing Han; Xueyu Tang
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 6: October 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

In order to cope with the nonlinear and non-Gaussian time series, a RBF-HMM-GMM model, which is based on radial basis function (RBF) neural networks with the assumption of measurement noise being hidden Markov model (HMM) and the distribution of each hidden states being approximated by Gaussian mixture models (GMM), is proposed in this paper. In the proposed model, both the orders (numbers of nodes and inputs of RBF network, numbers of hidden states of HMM, numbers of Gaussian mixture component of GMM) and the parameters change over time. Firstly, a scheme for time series forecasts based on RBF-HMM-GMM model is proposed. Then an on-line prediction algorithm based on RBF-HMM-GMM model using sequential Monte Carlo (SMC) methods is developed. At last, the monthly West Texas Intermediate crude oil future price series are analyzed, and experimental results indicate that the RBF-HMM-GMM model is able to predict the time series accurately. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1545 
Monte-Carlo SURE for Choosing Regularization Parameters in Image Deblurring Yubing Han; Kelan Wang; Mengna Xu
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 6: June 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Parameter choice is crucial to regularization-based image deblurring. In this paper, a Monte Carlo method is used to approximate the optimal regularization parameter in the sense of Stein’s unbiased risk estimate (SURE) which has been applied to image deblurring. The proposed algorithm is suitable for the exact deblurring functions as well as those of not being expressed analytically. We justify our claims by presenting experimental results for SURE-based optimization with two different regularization algorithms of Tikhonov and total variation regularization. Experiment results show the validity of the proposed algorithm, which has similar performance with the minimum MSE. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2675
Co-Authors Dongqing Zhang Kelan Wang Mengna Xu Xueyu Tang