ESN regularization model for discriminating mine water inrush source

LI Yuanzhi; NIU Guoqing; ZHANG Xuanxuan

doi:10.3969/j.issn.1001-1986.2018.01.019

Volume 46 Issue 1

Feb. 2018

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COAL GEOLOGY & EXPLORATION > 2018 > 46(1): 108-114. > DOI: 10.3969/j.issn.1001-1986.2018.01.019

LI Yuanzhi, NIU Guoqing, ZHANG Xuanxuan. ESN regularization model for discriminating mine water inrush source[J]. COAL GEOLOGY & EXPLORATION, 2018, 46(1): 108-114. DOI: 10.3969/j.issn.1001-1986.2018.01.019

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ESN regularization model for discriminating mine water inrush source

School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China

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Innovation Team Development Program of Ministry of Education (IRT_16R22)

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Received Date: May 04, 2017
Published Date: February 24, 2018

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Abstract

Aiming at the problem that the standard echo state neural network(ESN) is over-fitting due to the abnormal solution, six kinds of regularization methods are combined with ESN neural network and applied to discriminate mine water inrush source. The models were evaluated and compared with the standard ESN model. The results show that the ESN water source discrimination model is prone to over-fitting, and the accuracy of discrimination is only 49%~88%. The damping least squares singular decomposition method(DSVD) combined with generalized cross validation method(GCV) called as the regularization method can improve the accuracy of the model, the accuracy of the model is improved to 100%, the best accuracy is about 64% higher than that of the standard ESN model, and the stability is improved by about 61%, and the method is adaptable to the reserve pool, which can simplify the complex mapping of the model, improve the computational efficiency, and enhance the generalization ability of the ESN discrimination model. Therefore, the ESN water source discrimination model based on GSVD-GCV regularization can be used as a new method to determine the source of water inrush in a rapid and effective way.
- water inrush source discrimination,
- echo state networks,
- regularization,
- singular decomposition,
- cross validation

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