Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment....Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.Given the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time consumption.The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction.In this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input layer.Thereafter,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results.After obtaining the relevant attributes,the selected attributes are given to the next layer.Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns.Tversky similarity index outcomes are given to the output layer.Similarity value is used as basis to classify data as heavy network or normal traffic.Thus,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model.Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.展开更多
一引言期望效用理论虽然将投资者对风险的喜好表示为规范模型,但是许多研究都表明现实中投资者的决策不一定与期望效用理论所主张的相一致。现实中投资者具有＂赌博喜好＂,即过度评价像彩票一样的小概率却能带来巨额收益（损失）的事件...一引言期望效用理论虽然将投资者对风险的喜好表示为规范模型,但是许多研究都表明现实中投资者的决策不一定与期望效用理论所主张的相一致。现实中投资者具有＂赌博喜好＂,即过度评价像彩票一样的小概率却能带来巨额收益（损失）的事件。Tversky and Kahneman（1992）考虑到这一现象并提出了累计前景理论,Barberis and Huang（2008）在基于累计前景理论进行选好的投资者存在情形下,对均衡情况下证券价格进行了理论叙述。此后,许多学者对这一理论所提示的投资者＂赌博喜好＂现象进行实证研究,研究结果表明在美国和日本股市投资者中普遍存在该理论所提示的＂赌博喜好＂现象。展开更多
文摘Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.Given the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time consumption.The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction.In this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input layer.Thereafter,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results.After obtaining the relevant attributes,the selected attributes are given to the next layer.Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns.Tversky similarity index outcomes are given to the output layer.Similarity value is used as basis to classify data as heavy network or normal traffic.Thus,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model.Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.
文摘一引言期望效用理论虽然将投资者对风险的喜好表示为规范模型,但是许多研究都表明现实中投资者的决策不一定与期望效用理论所主张的相一致。现实中投资者具有＂赌博喜好＂,即过度评价像彩票一样的小概率却能带来巨额收益（损失）的事件。Tversky and Kahneman（1992）考虑到这一现象并提出了累计前景理论,Barberis and Huang（2008）在基于累计前景理论进行选好的投资者存在情形下,对均衡情况下证券价格进行了理论叙述。此后,许多学者对这一理论所提示的投资者＂赌博喜好＂现象进行实证研究,研究结果表明在美国和日本股市投资者中普遍存在该理论所提示的＂赌博喜好＂现象。
