Integrating the merit of wavelet transform with that of artificial neural network, a wavelet neural network (WNN) model for forecasting network traffic was created.
结合小波变换和人工神经网络的优势，建立一种网络流量预测的小波神经网络模型。
Finally it proves by using the actual data that this model may use in the road traffic accident forecasting.
最后通过实际数据验证证明了该模型可以用于道路交通事故预测。
Aiming at solving the demanding problem of road traffic system in exactly forecasting the traffic freight volume, an introduction is made into the Grey Model GM (1, 1) for forecasting.
针对公路交通系统在准确预测交通运量时的棘手问题，引进灰色预测GM(1,1)模型进行预测。
The traffic flow forecasting model based on neural network has been applied widely in its because of its high forecasting accuracy and self-learning ability.
基于神经网络的交通流预测模型已被广泛应用于ITS由于其较高的预测精度和自我学习能力。
Based on the dynamic and stochastic characteristic of short-term traffic volume, an approach combined wavelet analysis and fuzzy Markov forecasting model is put forward.
基于短时交通量时间序列的随机波动特征，提出一种小波分析和模糊马尔柯夫结合的预测方法。
Moreover, SVM-based forecasting model performs faster than ARMA model to forecast the communication traffic. Generally speaking, the overall performance of SVM model is optimal.
而且SVM的预测速度明显比arma模型快，综合各方面考虑，SVM预测模型的整体性能最优。
Research on Dynamic Traffic Flow Forecasting Model and Algorithm in Large Scale Transportation Network;
提出了大规模网络中一种基于相似度的异常检测模型。
Research on Dynamic Traffic Flow Forecasting Model and Algorithm in Large Scale Transportation Network;
提出了大规模网络中一种基于相似度的异常检测模型。
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