A rapid identification model of mine water inrush based on extreme learning machine

被引：0

作者：

Wang Y. ^{[1
,2
]}

Zhou M. ^{[1
]}

Yan P. ^{[1
]}

Hu F. ^{[1
]}

Lai W. ^{[1
]}

Yang Y. ^{[3
]}

Zhang Y. ^{[4
]}

机构：

[1] College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan

[2] School of Computer and Information, Fuyang Teachers College, Fuyang

[3] School of Resources and Geosciences, China University of Mining and Technology, Xuzhou

[4] Xieqiao Coal Mine, Huainan Mining Group, Fuyang

来源：

Meitan Xuebao/Journal of the China Coal Society | 2017年 / 42卷 / 09期

关键词：

Extreme learning machine; Laser induced fluorescence spectra; Mine water inrush; Principal component analysis; Water source identification;

D O I：

10.13225/j.cnki.jccs.2017.0577

中图分类号：

学科分类号：

摘要：

In the process of disaster prevention of coal mine water inrush, it is necessary to quickly and accurately identify the types of water sources. The technology of laser induced fluorescence has the characteristics of high sensitivity, rapid and accurate for monitoring, and it also obtains the fluorescence spectra of water samples. After preprocessing spectra with Savitzky-Golay algorithm and feature extraction with principal component analysis, the multi-classification learning model is established by the extreme learning machine algorithm. The Sigmoid function is determined as hidden layer activation function, and the optimal number of hidden layer nodes is determined through the cross validation method. From the average time of training network, the average accuracy of classification and the standard deviation of accuracies, the performance is compared with the conventional classification algorithms such as BP and SVM. The results show that the model is consistent with the conventional classification model on the average accuracy of classification in the training and test set. While the standard deviation of accuracies is minimum, it shows that the model has the stable performance of classification. When training the model, the learning time is greatly reduced. Therefore, the model is more suitable for the rapid and accurate classification of water inrush sources. © 2017, Editorial Office of Journal of China Coal Society. All right reserved.

引用

页码：2427 / 2432

页数：5

共 17 条

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