【摘要】 研究短交通流量预测问题,短时交通流量数据中含有大量噪声,对预测精度产生不利影响,为了提高短交通流量预测精度,提出一种小波消噪的神经网络短时交通流量预测模型。首先采用小波技术对短时交通流量数据进行消噪处理,然后采用关联维数确定BP神经网络输入变量个数,最后采用BP神经网络建立短时交通流量预测模型。仿真结果表明,与消噪前比,消噪后模型的预测精度有了较显著提高,其预测误差远远小于消噪前,预测结果更具实用价格。更多还原

【Abstract】 The paper put forward a short-term traffic flow forecasting model based on wavelet neural network for.Firstly,the wavelet was usd to carry out the de-noising for the short-time traffic flow data,and then the correlation dimension was used to determine the number of input variables of BP neural network.Finally,the BP neural network model was used as short-term traffic flow forecasting model.The simulation results show that the prediction accuracy of the denoising model is significantly improved,and the prediction error is far less than that of non-denoising.更多还原