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Proceedings Paper

Fuzzy neural network model applied in the mine water inrush prediction
Author(s): Jian-yu Xiao; Min-ming Tong; Qi Fan; Chang-jie Zhu
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Paper Abstract

Combining with fuzzy reasoning, neural network and back propagation algorithm, a fuzzy neural network model for the prediction of mine water inrush is built after the analysis of the main factors affected the mine water inrush, such as water pressure, fault throw, water conducted zone width, aquifer thickness and confining bed thickness. Then, on the basis of the water inrush mechanism and some practical examples, influential factors on mine water inrush are divided into three groups, and after that 81 fuzzy inference rules are constructed effectively. And then, the four-layer back propagation fuzzy neural network takes effect in training the input variables. Finally, a simulated prediction of the fuzzy neural network is made by using test samples. The results of simulation show that mine water inrush model based on fuzzy neural network is superior to the traditional BP neural network on training speed and predicting precision.

Paper Details

Date Published: 20 August 2010
PDF: 7 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78203O (20 August 2010); doi: 10.1117/12.866853
Show Author Affiliations
Jian-yu Xiao, China Univ. of Mining and Technology (China)
Huaibei Normal Univ. (China)
Min-ming Tong, China Univ. of Mining and Technology (China)
Qi Fan, Huaibei Normal Univ. (China)
Chang-jie Zhu, Huaibei Normal Univ. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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