Shearer memory cutting strategy research basing on GRNN

摘要：

Shearer height adjusting is a key technology for coalmine unmanned working face. On the basis of establishing Shearer working face mathematical models, this paper determined related parameters influencing the Shearer height adjusting, then analyzed traditional Shearer memory cutting strategy and pointed out its shortcomings. Aiming at changing technical limitations of Shearer height adjusting currently, this paper proposed a new Shearer memory cutting strategy based on GRNN(General Regression Neural Network). According to height adjusting data acquired from Shearer working face, we use MATLAB to analyze the new Shearer memory cutting algorithm, results shows GRNN network approximation error is ±0.02m, and GRNN network prediction error is ±0.025m. The experimental result shows that the new Shearer memory cutting strategy has higher prediction accuracy and better generalization ability.

关键词： shearer memory cutting GRNN

作者: FAN Qi-gao LI Wei WANG Yu-qiao ZHOU Li-juan SU Xiu-ping YANG Xue-feng YE Guo

作者单位: School of Mechanical & Electrical Engineering China University of Mining & Technology Xuzhou, Jiangsu 221008, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 96-99

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）