改进人工蜂群算法优化RBF神经网络的短时交通流预测

为了提高径向基函数RBF神经网络预测模型对短时交通流的预测准确性,提出了一种基于改进人工蜂群算法优化RBF神经网络的短时交通流预测模型。利用改进人工蜂群算法确定RBF网络隐含层的中心值以及隐含层单元数,然后训练改进的人工蜂群算法RBF神经网络预测模型,并将其应用到某城市4天的短时交通流量数据的验证。将实验结果与传统RBF神经网络预测模型、BP神经网络预测模型和小波神经网络预测模型进行了比较。对比结果表明,该方法对短时交通流具有更高的预测准确性。

Short term traffic flow prediction of optimized RBF neural networks based on the modified ABC algorithm

In order to improve the prediction accuracy of radial basis function (RBF) neural network model for short term traffic flow, we propose a prediction model for short term traffic flow of optimized RBF neural networks based on the modified artificial bee colony (ABC) algorithm. The modified ABC algorithm is used to confirm center value and unit numbers of the hidden layers of the RBF neural networks. Then the modified RBF neural network prediction model is trained, and the efficiency of the proposed prediction model is tested through simulations on the short term traffic flow data of a city in four days. Experimental results of the proposed model are compared with the traditional RBF neural network model, the BP neural network model and the wavelet neural network model, which verify the higher prediction accuracy of the proposed method.