摘要
在基于模糊神经网络的交通流量预测中,神经网络的各节点参数优化是最关键的。采用粒子群算法优化模糊神经网络的参数。针对粒子群算法易于陷入局部最优的缺点,提出一种改进的粒子群优化算法,并将改进的算法用于路口交通流量预测。仿真结果表明,该算法的收敛速度和预测精度优于传统粒子群算法、BP算法,提高了交通流量预测的精度和速度。
In a traffic flow prediction based fuzzy neural network,the optimization of the parameters of the nodes is very critical.An improved particle swarm optimization method was used to optimize the fuzzy neural network parameters to improve the precision and speed of vehicle prediction by fuzzy neural networks.Simulation results show that the accuracyof method is faster and accuracy is more accurate than PSO and BP algorithm,and the fuzzy neural network prediction model based on IPSO is an effective method for traffic flow prediction.
出处
《计算机科学》
CSCD
北大核心
2012年第10期190-192,230,共4页
Computer Science
基金
国家自然科学基金项目(60903159)
中央高校基本科研业务费专项资金项目(N100604012)资助
关键词
模糊神经网络
粒子群优化
交通流量预测
Fuzzy neural network
Particle swarm optimization
Traffic flow prediction
