基于RBF神经网络的交通流量预测算法

Traffic volume forecasting algorithm based on RBF neural network

传统的径向基函数神经网络构造算法大多是根据先验知识和以往的经验事先确定网络的隐层结构,采用传统聚类和最小二乘法训练网络的各项参数,这种算法一般是基于局部搜索机制,使得训练的参数往往陷入局部极小值.提出用遗传算法结合一种新的聚类方法即最疏集(MSS-most scattered set)均值聚类算法和传统的最小二乘法来训练RBF(radial basis function)网络结构参数的方法.该方法不仅避免了网络训练陷入局部极小的问题,而且新的聚类方法的计算效率有所提高.通过把该算法应用在交通流预测方面,取得了令人满意的效果.

Traditional training algorithms for radial basis function （RBF） neural networks usually start with a predetermined hidden layer structure, which is selected by using a priori knowledge and based on previous experience. The parameters of RBF networks are trained by using traditional clustering and the least squares method. These training algorithms are always based on the local search method and often suffer from being trapped at structural local minima. A new method for training RBF structural parameters by using a genetic algorithm is put forward, of which a new clustering method named the sorting MSS （most scattered set） cluster- ing method and a traditional least square method are incorporated. This method can not only prevent the result of the network from being trapped at local minima but also highly improves the computational efficiency. It gives satisfactory results when this algorithm is applied to traffic flow forecasting.