机译:时空数据特征不确定的交通流量预测的新型模糊深度学习方法
College of Computer Science and Electronic Engineering, Hunan University,College of Information and Electronic Engineering, Hunan City University;
College of Computer Science and Electronic Engineering, Hunan University;
College of Computer Science and Electronic Engineering, Hunan University;
College of Computer Science and Electronic Engineering, Hunan University;
College of Computer Science and Electronic Engineering, Hunan University;
Department of Computer and Information Sciences, Fordham University;
College of Computer Science and Electronic Engineering, Hunan University,Department of Computer Science, State University of New York;
Deep learning; Fuzzy representation; Residual networks; Traffic flow prediction;
机译:动态空间 - 时间特征优化与ERI大数据进行短期交通流量预测
机译:通过深度卷积神经网络来探索空间关系,用于不完整数据的流量预测
机译:从小区传输流量模型到排放预测的不确定性传播:一种数据驱动的方法
机译:基于支持向量机的突出空间 - 时间两维数据融合,用于异常状态的交通流预测
机译:具有时空特征的实时短期交通速度预测的深度学习方法
机译:利用微阵列数据通过特征选择和模糊c均值聚类进行肿瘤分类和标记基因预测
机译:从小区传输交通流模型到排放预测的不确定性传播:数据驱动方法