Title :
Research of mine ventilation resistance calculation based on BP neural network
Author_Institution :
Sch. Of Safety Sci. & Eng., Henan Polytech. Univ., Jiaozuo, China
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;
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
DOI :
10.1109/AIMSEC.2011.6011339