基于遗传算法径向基神经网络的交通流预测

为提高径向基（RBF）神经网络预测模型对交通流预测的准确性，提出了一种基于遗传算法优化径向基神经网络的交通流预测方法。利用遗传算法优化径向基神经网络的权值和阈值，然后训练RBF神经网络预测模型以求得最优解，并将该预测方法与RBF神经网络和BP神经网络的预测结果进行对比。仿真结果表明，该方法对交通流具有较好的非线性拟合能力，预测精度高于径向基神经网络和BP神经网络。

Prediction of traffic flow of optimized radial basis function neural network based on genetic algorithm

In order to improve the prediction accuracy of radial basis function （RBF） neural network model for predicting traffic flow, a prediction method for traffic flow of optimized RBF neural network based on genetic algorithm （GA） is presented. The GA is used to optimize the weights and thresholds of RBF neural network, and the RBF neural network is trained to search for the optimal solution. The efficiency of the proposed prediction method is tested by comparison with the results predicted by RBF and BP neural network. The simulation results show that the proposed method has better nonlinear fitting ability for the predic- tion of traffic flow. Moreover, it has better fitting ability and higher accuracy than RBF and BP neural network.