This paper presents a hybrid model for urban arterial travel time prediction based on the so-called state space neural networks (SSNN) and the extended Kalman Filter (EKF).
提出了一种基于状态空间神经网络(SSNN)和拓展卡尔曼滤波(ekf)的混合式行程时间预测模型。
Based on the robust estimation in the state-space and the adaptive modification of prediction covariance matrix, a new algorithm was proposed to track Thevenin equivalent parameters.
融合预报协方差矩阵的自适应调整和状态空间鲁棒性估计,给出辨识戴维南等值参数的新算法。
Based on the robust estimation in the state-space and the adaptive modification of prediction covariance matrix, a new algorithm was proposed to track Thevenin equivalent parameters.
融合预报协方差矩阵的自适应调整和状态空间鲁棒性估计,给出辨识戴维南等值参数的新算法。
应用推荐