Study of the Gas Emission Amount Based on Gray Elman Neural Network
Gas emission amount prediction is essential and important for mine ventilation design and making gas prevention measure, and its prediction efficiency directly determines safety production level and economic efficiency. It is known that gas emission amount is related with many factors, with modern computer science technology developing, more and more mathematics theories are extended to improve the prediction efficiency. This paper will introduce Grey theory into Elman artificial neural network and combined them together to establish the gas emission prediction model based on gray Elman artificial neural network and simulated with the software Matlab. After training and test results, it shows that the prediction results with this way are nearly factual and have more advantage over the traditional grey prediction model. And this method is applied to less original data and the history data with sudden leaping, which makes the prediction results more reliable, accurate and can have a great guide with the practie.
grey theory neural network gas emission amount prediction
ZHANG Jing-gang WANG De-guang CHEN Yi YUAN Zheng-Ian
College of Safety Engineering, North China Institute of Science & Technology, Beijing 101601, China Institute of Chemical Industry, Jinan 250014, China Department of Electronic Information Engineering, North China Institute of Science & Technology, Bei University of Science & Technology Beijing, Beijing 100083, China
国际会议
The 2nd International Conference on Mine Safety and Environment Protection(第二届采矿、安全与环境保护国际会议)
西安
英文
225-230
2011-10-10（万方平台首次上网日期，不代表论文的发表时间）