The paper sets up a time sequence model of laser gyro random error and processes the drift data by Kalman filter based on the model.
在对激光陀螺漂移数据建立时间序列模型的基础上，对激光陀螺的漂移数据进行了卡尔曼滤波。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
The synergetic method mainly includes: noise-separating method based on time sequence model of atomic clock and malfunction - separating and positioning method based on malfunction-tree.
该方法的内容包括：原子钟一阶模型的噪声分离方法和故障树的故障分离、故障定位方法。
Based on temporal reasoning thought, the model proposed fault path search strategy considering time sequence, which enabled the model to have temporal characteristic.
该模型基于时序推理思想，给出了考虑时序性的故障路径搜索策略，从而使模型具有时序特征。
The results show that the filter based on the time sequence model can effectively decrease the random error.
结果表明，基于时间序列模型的卡尔曼滤波器有效地减小了随机误差。
The thesis studied the BP neural network's model and structure, its studying rule, built up the time sequence based on BP neural network. Also studied the problem like network size, generalization.
本论文探讨了BP神经网络的模型与结构，BP学习规则，构建了基于BP神经网络的时间系列预测模型，研究了神经网络的规模、推广能力等问题。
The thesis studied the BP neural network's model and structure, its studying rule, built up the time sequence based on BP neural network. Also studied the problem like network size, generalization.
本论文探讨了BP神经网络的模型与结构，BP学习规则，构建了基于BP神经网络的时间系列预测模型，研究了神经网络的规模、推广能力等问题。
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