电讯技术2017，Vol.57Issue(1)：1-8,8.DOI:10.3969/j.issn.1001-893x.2017.01.001
基于改进型深度学习的流量预测
Traffic Prediction Based on Modified Deep Learning
摘要
Abstract
To solve the problem of low traffic prediction accuracy in wireless networks,a method based on the adaptive grouping stacked auto-encoders( AG-SAEs) deep learning is proposed. In data preprocess-ing,the maximum and minimum method is used to normalize the data,and a novel adaptive grouping meth-od is adopted to divide the normalized data into different groups adaptively. Then,a multi-input multi-out-put prediction model based on the deep learning model is established. All the groups are input to the stacked auto-encoder model to train the model and map the relationship between input and output traffic. Finally,in order to further improve the prediction accuracy,the modified Newton method is used to update the weight parameters in the model training section. The simulation experiment and comparison with other methods show that the proposed method processes a smaller prediction relative error.关键词
认知网络/流量预测/深度学习/自适应分组Key words
cognitive network/traffic prediction/deep learning/adaptive grouping分类
信息技术与安全科学引用本文复制引用
朱江,宋永辉,刘亚利..基于改进型深度学习的流量预测[J].电讯技术,2017,57(1):1-8,8.基金项目
国家自然科学基金资助项目(61271260) （61271260）
重庆市科委自然科学基金资助项目(cstc2015jcyjA40050) （cstc2015jcyjA40050）
