【摘要】 准确可靠的交通信息预测是实现智能交通诱导和交通管理的关键。本文提出一种两步学习算法:遗传-梯度算法,用于RBF神经网络的学习,并应用到交通信息预测中。算法充分利用遗传算法的全局优化能力和梯度下降算法的局部搜索能力,一方面加快网络收敛速度,另一方面优化网络结构,并在一定程度上提高网络的推广能力,宁波市实时交通速度信息预测的实验结果论证该算法的有效性。更多还原

【Abstract】 The real-time and accurate predicted traffic information is critical to the intelligent traffic inducement and the traffic management. A two-step learning algorithm GGA (Genetic Gradient Algorithm) for radial basis function (RBF) neural network is proposed in this paper. A genetic algorithm (GA) initially determines the parameters of the RBF network including the number and locations of the selected centers and the widths of Gaussian kernel functions in the hidden layer. Then a gradient descent algorithm is adopted to further adjust these parameters of the RBF network. A smaller network with better generalization capability can thus be obtained. The experimental results of the real-time traffic information prediction in Ningbo city show good performance of the proposed method.更多还原