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Mine water discharge prediction based on least squares support vector machines 被引量:1
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作者 guo xlaohui MA Xiaoping 《Mining Science and Technology》 EI CAS 2010年第5期738-742,共5页
In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio ... In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge. 展开更多
关键词 混沌时间序列预测 最小二乘支持向量机 矿井水 排放 相空间重构理论 预测模型 统计学习理论 SVM
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