Research of mine ventilation resistance calculation based on BP neural network

Author

Zhang, Pan

Author_Institution

Sch. Of Safety Sci. & Eng., Henan Polytech. Univ., Jiaozuo, China

fYear

2011

fDate

8-10 Aug. 2011

Firstpage

3226

Lastpage

3229

Abstract

Mine ventilation resistance is decided to coefficient of frictional resistance. and it is very vital to airflow distribution of using air place, With the special conditions of roadways of I-beam supporting in the article, the coefficient of frictional resistance is calculated through an improved BP artificial neural network model, and the article analyses the difference between BP neural network calculation and actual measure. Suggest to apply the BP artificial neural network on pattern recognition of coefficient of frictional resistance. The main advantages of it include simple structure, efficient convergence, precise forecasting, etc. The precision in calculation of model is higher, it has a certain practical value.

Keywords

backpropagation; friction; mining industry; neural nets; pattern recognition; ventilation; BP artificial neural network; airflow distribution; artificial neural network; frictional resistance coefficient; mine ventilation resistance calculation; pattern recognition; Artificial neural networks; Biological neural networks; Electrical resistance measurement; Immune system; Predictive models; Resistance; Ventilation; BP neural network; frictional resistance; mine ventilation;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on

Conference_Location

Deng Leng

Print_ISBN

978-1-4577-0535-9

Type

conf

DOI

10.1109/AIMSEC.2011.6011339

Filename

6011339