基于PSO-GRNN神经网络的煤矿井下定位算法研究  

Underground positioning algorithm of coal mine based on PSO-GRNN neural network

作　　者：史明泉[1] 崔丽珍[1] 赫佳星[1] SHI Mingquan;CUI Lizhen;HE Jiaxing(School of Information Engineering,Inner Mongolia University of Science&Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院

出　　处：《中国矿业》2020年第2期88-93,共6页China Mining Magazine

基　　金：国家自然科学基金项目资助(编号：61761038);内蒙古自治区科技计划项目资助(编号：201502013-1);内蒙古自治区自然基金项目资助(编号：2015MS0623)

摘　　要：为进一步提高井下定位精度,本文提出一种基于粒子群算法-广义回归神经网络(PSO-GRNN)的煤矿井下定位算法。该算法利用广义回归神经网络(GRNN)建立井下定位模型,通过粒子群优化算法(PSO)寻找广义回归神经网络最优的平滑因子,降低人为调整的影响,提高定位精度。将信标节点接收到的信号强度(RSSI)值输入训练好的神经网络,神经网络的输出就是待测节点的坐标。仿真实验表明,PSO-GRNN模型相比未经优化的GRNN模型和BP模型,定位精度更高;相比BP模型,算法复杂度更低,效率更高,满足井下自适应定位要求。A kind of underground positioning algorithm based on particle swarm optimization-generalized regression neural network(PSO-GRNN)is proposed.The proposed PSO-GRNN algorithm builds underground positioning model by the fast learning speed and strong approximation ability of generalized regression neural network(GRNN)and adjusted GRNN's smoothing parameters by using particle swarm optimization algorithm(PSO)to reduce the impact of human factors on selecting GRNN smoothing parameters to minimum.Finally,the coordinates of unknown nodes of underground can be directly obtained from the output of PSO-GRNN model.Simulating results show that the positioning accuracy of the PSO-GRNN model is better than that of the GRNN model and the BP model,and the algorithm complexity is lower and the efficiency is higher than that of the BP model,and meets the requirement of adaptive underground positioning.

关 键 词：煤矿 井下定位 广义回归神经网络 粒子群优化算法 

分 类 号：TD655[矿业工程—矿山机电]