量子神经网络在交通运输能力预测中的应用

Quantum Neural Network in Traffic Transportation Capacity Forecast Application

针对BP神经网络在道路交通运输能力预测中精度不足及收敛速度慢的问题,引入量子神经网络并构建道路交通能力预测模型.通过对以往数据的发展变化趋势进行分析建立现有数据与时间之间的对应关系,然后再利用这组数据,最后对预测的结果进行分析,预测结果表明:该预测模型能够较好地适应道路交通运输能力数据的特性,且预测精度和收敛速度较改进BP神经网络有显著提高。

Based on the BP neural network in road transportation capacity forecast precision is insufficient and the slow convergence problem, by introducing the quantum neural network and the construction of road traffic capacity prediction model. According to the previous data development trend analysis of the establishment of the existing data and temporal correspondence between, and then use this data set, most of pretest node fruit into rows of sensor node, the fruit table Ming： the predictive model can better adapt to the characteristics of the data of road transportation capacity, and the forecast precision and the convergence speed is improved BP neural network was improved significantly.