计算机科学 ›› 2012, Vol. 39 ›› Issue (10): 190-192.

• 人工智能 • 上一篇    下一篇

基于IPSO的模糊神经网络优化及交通流量预测

金成钧,常桂然,程维,姜慧研   

  1. (东北大学信息科学与工程学院 沈阳110819) (东北大学软件学院 沈阳110819)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Fuzzy Neural Networks Based on IPSO for Traffic Flow Prediction

  • Online:2018-11-16 Published:2018-11-16

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、PP算法,提高了交通流量预测的精度和速度。

关键词: 模糊神经网络,粒子群优化,交通流量预测

Abstract: 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.

Key words: Fuzzy neural network, Particle swarm optimization, Traffic flow prediction

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