摘要
实时交通流预测是智能运输系统研究的重要内容之一.本文将小波分析的相关知识与模糊神经网络相结合,给出了基于小波模糊神经网络的交通流预测模型,采用小波函数作为模糊隶属度函数,用神经网络来实现模糊推理,完成对下一个周期性交通流的估计.同时,用遗传算法来优化整个网络,实测数据验证这种方法预测精度高,收敛过程平稳,适应性强.
Real-time traffic flow prediction is one of important contents of intelligent transportation system research. Combined with the related knowledge of wavelet analysis and fuzzy neural networks, this paper gives the traffic flow forecasting model based on wavelet fuzzy neural networks. It takes wavelet function as fuzzy membership function, uses neural networks to realize fuzzy reasoning, and finishes the estimation of next cyclical traffic flow. Simultaneously the genetic algorithm is used to optimize the overall network. After the field data test, this method has high forecasting precision, stable convergence process, strong adaptability.
出处
《计算机系统应用》
2016年第7期161-164,共4页
Computer Systems & Applications
基金
绥化学院科学技术研究项目(K1401012)
黑龙江省教育厅科学技术研究项目(12533077)
关键词
小波模糊神经网络
交通流
预测
遗传算法
wavelet fuzzy neural network
traffic flow
prediction
genetic algorithm
