计算机应用与软件2016，Vol.33Issue(8)：136-140,182,6.DOI:10.3969/j.issn.1000-386x.2016.08.030
基于灰色-RBF 神经网络的传播损耗模型训练
PROPAGATION LOSS MODEL TRAINING ALGORITHM BASED ON GREY-RBF NEURAL NETWORK
摘要
Abstract
Indoor signal propagation loss model is the key to the radio frequency identification (RFID)localisation technology based on received signal strength indicator (RSSI).Because of the rather complex indoor environment and the influence of multipath effect,traditional empirical signal propagation loss model has poor environmental adaptability,and this leads to bigger localisation error in ranging.Besides the training of propagation loss model using traditional neural network has the disadvantages of too much training samples required and heavy collection workload in hardware.To overcome the problems mentioned above,we put forward the training method for propagation loss model in variable density sampling mode,which is based on the combination of grey theory and radial basis function (RBF)neural network.Based on grey theory,more training samples can be forecasted by using fewer samples,and they are used together with part of the original samples to reconstruct the sample data for RBF neural network training,so as to build the propagation loss model.Experimental results show that by using the proposed method,it is able to build the indoor signal propagation loss model accurately with less training samples,which can well meet the precision requirement of the indoor localisation and greatly reduce the workload of sample collection as well.关键词
室内定位/射频识别/传播损耗模型/径向基神经网络/灰色理论Key words
Indoor localisation/Radio frequency identification (RFID)/Propagation loss model/Radial basis function (RBF)neural network/Grey theory分类
信息技术与安全科学引用本文复制引用
李丽娜,梁德骕,马俊,涂志..基于灰色-RBF 神经网络的传播损耗模型训练[J].计算机应用与软件,2016,33(8):136-140,182,6.基金项目
国家自然科学基金青年项目（61403176）；辽宁省教育厅科学技术研究项目（L2013003）。 （）
