Mine water discharge prediction based on least squares support vector machines

被引：0

作者：

Guo X. ^{[1
]}

Ma X. ^{[2
]}

机构：

[1] School of Computer Science and Technology, Xuzhou Normal University

[2] School of Information and Electrical Engineering, China University of MiningandTechnology

来源：

Mining Science and Technology | 2010年 / 20卷 / 05期

关键词：

chaotic time series; LS-SVM; mine water discharge; phase space reconstruction; prediction;

D O I：

10.1016/S1674-5264(09)60273-8

中图分类号：

学科分类号：

摘要：

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. © 2010 China University of Mining and Technology.

引用

页码：738 / 742

页数：4

共 16 条

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