基于灰色人工神经网络组合模型的交通量预测

针对公路远景交通量预测工作中常存在交通量原始数据呈随机性、非线性变化的特点,同时学习样本量较小、信息不充分的问题,充分利用贝叶斯正则化神经网络非线性逼近,良好的泛化能力和无偏GM(1,1)模型的少数据建模,弱化原始数据随机性并增强规律性,消除了传统GM(1,1)模型预测所固有的偏差的优点,建立无偏GM(1,1)-贝叶斯正则化神经网络交通量组合预测模型,并应用于实际交通量预测中.与传统BP预测模型比较,算例结果表明所建模型有效可行,提高了预测精度.

TRAFFIC VOLUME FORECAST BASED ON COMBINED MODELS OF GRAY SYSTEMS AND ARTIFICIAL NEURAL NETWORKS

In the long-term forecasting work, the original data has the characteristics of randomicity and non-linear movement, and also the capacity of available study samples is small and information is insufficient. The bayesian-regularization neural network possesses the characteristics of strong nonlinear fitting and the capabilities of excellent generalization. Unbiased GM(1,1) can use few data to construct models, it can weaken the randomicity of the original data and strengthen regularity, and also can eliminate the inherent deviation of the conventional GM(1,1) model. Making the best use of the merits of the two, the combined model of unbiased GM(1,1) and bayesian-regularization neural network are constructed and put into real traffic forecasting work. By contrasting with BP network, the result shows that this model is feasible and efficient, the accuracy of forecasting is also increased.