广义回归神经网络在交通运输量预测中的应用

摘要

因为交通运输、地方经济及企业发展的紧密联系,交通运输量预测成为运输市场和经济发展研究中的一个重要问题.以影响交通运输量的若干重要参数作为样本数据,构建一个广义回归神经网络,经过学习训练后使之达到误差最小,再以此网络外推出未来的交通运输量.结果证明广义回归神经网络用于交通运输量预测的有效性.%As there are close contact among transportation, local economic and enterprise development, the forecast of the transport volume has become an important issue of transport market and economic development research. In this paper, a Generalized Regression Neural Network(GRNN)is built taking a number of important parameters that affect transport volume as the sample datas. After studying and training to meet the minimum error, this network will forecast the future traffic volume. The result demonstrates the effectiveness of using GRNN to forecast transport volume.