Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material  

作　　者：CHANG Qing-liang ZHOU Hua-qiang HOU Chao-jiong 

机构地区：[1]School of Mines, China University of Mining ＆ Technology, Xuzhou, Jiangsu 221008, China

出　　处：《Journal of China University of Mining and Technology》2008年第4期551-555,共5页中国矿业大学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (No. 50490270, 50774077, 50574089, 50490273);the New Century Excellent Personnel Training Program of the Ministry of Education of China (No. NCET-06-0475);the Special Funds of Universities outstanding doctoral dissertation (No. 200760) ;the Basic Research Program of China (No. 2006CB202204-3)

摘　　要：In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural net- works. Based on cases related to our test data of filling material, the predicted results of the model and measured values are com- pared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.In order to forecast the strength of filling material exactly, the main factors affecting the strength of filling material are analyzed. The model of predicting the strength of filling material was established by applying the theory of artificial neural networks. Based on cases related to our test data of filling material, the predicted results of the model and measured values are compared and analyzed. The results show that the model is feasible and scientifically justified to predict the strength of filling material, which provides a new method for forecasting the strength of filling material for paste filling in coal mines.

关 键 词：mining engineering paste filling material neural network particle swarm optimized algorithm prediction 

分 类 号：TD8[矿业工程—矿山开采]