基于蚁群粒子群的模糊神经网络交通流量预测

实时准确的交通流量预测是智能交通诱导和交通控制实现的前提和关键。针对城市交通流的特点,建立了模糊神经网络预测模型,并将全局优化的蚁群算法和粒子群算法组成递阶结构优化模糊神经网络的参数。算法中,主级为蚁群算法,进行全局搜索;从级为粒子群算法,进行局部搜索。仿真结果表明该模型能够取得比梯度下降法更高的预测精度。

Fuzzy neural network model for forecasting short-time traffic flow based on ant algorithm and Particle Swarm Optimization

Real-time and accurate traffic flow prediction is very important to the intelligent traffic guidance and control.According to the characteristics of short-time traffic flow,a fuzzy neural network model has been proposed to solve short-time traffic flow prediction.The paper combines Particle Swarm Optimization(PSO) algorithm with ant algorithm for training the fuzzy neural network.The algorithm is formulated in a form of hierarchical structure.The master level is ant algorithm and slave level is PSO.The global search is performed at the master level,while the local search is carried out at the slave level.The simulation results demonstrate the proposed model can improve prediction accuracy,compared with BP based training techniques.