关键词: 分态预测, 相空间重构, 混沌和非混沌, 支持向量机, 神经网络

Abstract: Traditional single prediction model in the coalmine gas concentration prediction can’t objectively and accurately reflect its change law because there are many factors that affect the change of gas concentration and internal relationship is complex. In order to effectively improve the gas concentration prediction precision, a coalmine gas concentration of sub-state prediction model is proposed. Using the maximum Lyapunov exponent the gas concentration time series are divided into non-chaotic state and chaotic state, then the improved Least Squares Support Vector Machines（LS-SVM） and neural network based on the Radial Basis Function（RBF） are used for modeling and training parameters optimization. It gets the best prediction model and the gas concentration time series prediction simulation experiment is carried out. The results show that the sub-state prediction model improves the prediction accuracy effectively and reduces the prediction error, the method can be more objective and accurate to forecast the gas concentration.

Key words: sub-state prediction, phase space reconstruction, chaotic and non-chaotic, Support Vector Machine（SVM）, neural network