Application of Neural Network in Predication Model of Flotation Indicators

摘要：

According to the floatation processing characteristic with time-variation, uncertainty and complicated nonlinear relations, a prediction method of concentrate grade and prediction model of ore dressing date is proposed. This article establish a prediction model of ore dressing date based on Jordan neural network including input of influence factors and dynamic time sequence feedback of concentrate grade, by combining BP algorithm with the temporal difference methods. The results applied in industry indicate that predictive precision is high, error is small, and stability is high. It has practical value, the application is successful.

关键词： Neural network Prediction model Ore dressing date BP algorithm TD method

作者: Yanqiong Tu Guanghua Ai Xiuxiang Tao Wangsheng Fang

作者单位: School of Information Engineering Jiangxi University of Science and Technology Ganzhou,China School of Resources and Environmental Engineering Jiangxi University of Science and Technology Ganzh School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou,China School of Information Engineering Jiangxi University of Science and Technology Ganzhou,Jiangxi,China

会议类型: 国际会议

会议名称: 2011 3rd IEEE International Conference on Computer Research and Development(ICCRD 2011)(2011第三届计算机研究与发展国际会议)

会议地点: 上海

会议语种:英文

页码: 196-199

在线出版日期: 2011-03-11（万方平台首次上网日期，不代表论文的发表时间）