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。
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).
该文根据生物神经元状态变化导致人脑空间结构和状态变化这一原理，提出了一种自适应构造神经网络的新方法。
According to the principle of biological neuron, which state influences the condition of the brain, a new method is presented to adaptively construct neural networks.
针对强化学习在连续状态和动作空间的泛化问题，人工神经网络是一种有效的解决方法。
And artificial neural network is a valid method to solve the generalization problem for the continuous state and action pairs in the reinforcement learning method.
该神经网络模型包含内部状态神经元的反馈并具有状态空间形式。
The neural network model contains the feedback of the state neurons and takes the form of state space representation.
然而，状态空间神经网络需要大量的历史数据作为离线训练之用。
However, the SSNN models required offline training with large data sets of input-output data.
然而，状态空间神经网络需要大量的历史数据作为离线训练之用。
However, the SSNN models required offline training with large data sets of input-output data.
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