Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Fuzzy Weighted Support Vector Regression With a Fuzzy Partition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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Gas concentration is one of key factors which influence safe production of coal mine, and it is very important to forecast gas concentration accurately for ensuring the coal mine safety. A novel approach is presented to forecast gas concentration based on support vector regression (SVR) in correlation space reconstructed by kernel principal component analysis (KPCA). A two-stage architecture is proposed to improve its prediction accuracy and generalization performance for gas concentration forecasting. In the first stage, KPCA is adopted to extract features and obtain kernel principal components, so the correlation space of gas concentration is reconstructed according to the accumulative contribution ratio. Then, in the second stage, support vector regression (SVR) is employed for forecasting gas concentration, which hyperparameters are selected by adaptive chaotic cultural algorithm (ACCA). The approach is compared with the forecasting model in whole space of gas concentration. The simulation shows that SVR model in correlation space using KPCA performs much better than that without correlation analysis.