计算机应用研究2017，Vol.34Issue(12)：3797-3800,4.DOI:10.3969/j.issn.1001-3695.2017.12.063
基于随机Dropout深度信念网络的移动用户行为识别方法
Human activity recognition method based on random Dropout deep belief network
摘要
Abstract
For the problem of mobile user activity model over-fitting which leads to the generalization of the problem,this paper put forward an improved mobile user activity recognition method of Dropout DBN (deep belief network).The method randomly changed the probability parameters in algorithms of Dropout,reduced the number of hidden units of network nodes,optimized the network weight in each training.This method improved the accuracy of recognition and generalization when the number of sample decreases.Experimental results show that the average recognition accuracy rate for five activities walking,running,upstairs and downstairs reaches 94.23％ by using random Dropout network,recognition accuracy improves 4.57％.关键词
行为识别/深度信念网络/深度学习/DropoutKey words
activity recognition/deep belief network/deep learning/Dropout分类
信息技术与安全科学引用本文复制引用
王忠民,王希,宋辉..基于随机Dropout深度信念网络的移动用户行为识别方法[J].计算机应用研究,2017,34(12):3797-3800,4.基金项目
国家自然科学基金资助项目(61373116) （61373116）
陕西省科技统筹创新工程计划项目(2016KTZDGY04-01) （2016KTZDGY04-01）
西安邮电大学研究生创新基金项目(103-602080006) （103-602080006）
西安市科技局科技计划项目[2017084CG/RC047(XAYD001)] （XAYD001）
