Model of soft measurement of grinding and classification system based on RBF neural network and RS theory
1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730000, China; 2.Department of Computer Science, Gansu Institute of Political Science and Law, Lanzhou, Gansu 730070, China)
Abstract： In order to increase the efficiency of mineral processing and improve the quality of dressing products on nickel sulfide, attributes reduction of multidimensional data in certain ore plant was studied by using rough set theory. Based on the relevant RBF neural network predicting model, least knowledge expressions was given to express inherent law of ore grinding. The metallurgical performance was built by applying the model. The results show that attributes reduction of multidimensional data is feasible on grinding and classification system. The model is helpful for learning inherent law of mineralprocessing, and theoretical basis of experimential operating is proved. Key parameters of ball mill and hydro cyclone were obtained by soft measurement technology. The analysis course is brief, the time of network learning and training is short, and learning precision is high. Simulation shows that estimating value is very close to analysis value.
王云峰, 李战明, 袁占亭, 包广清. 基于RBF神经网络和RS理论的磨矿分级系统软测量模型[J]. 江苏大学学报（自然科学版）, 2010, 31(6): 695-699.
WANG Yunfeng, LI Zhanming, YUAN Zhanting, BAO Guangqing. Model of soft measurement of grinding and classification system based on RBF neural network and RS theory[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2010, 31(6): 695-699.
2010, Vol. 31 
