| 改进PSO优化RBFNN的短时交通流量预测方法 |
| |
| 引用本文： | 董海,游婷,李小琛.改进PSO优化RBFNN的短时交通流量预测方法[J].数字社区&智能家居,2021(5). |
| |
| 作者姓名： | 董海 游婷 李小琛 |
| |
| 作者单位： | 福建江夏学院设计与创意学院 |
| |
| 摘 要： | 针对短时交通流的预测精度问题,该文用PSO算法优化RBFNN模型的基础上,引入学习因子优化策略对PSO算法改进,进一步提高预测精度。该文针对PSO算法中认知因子和社会因子在全局搜索和局部搜索的不同作用,对非线性的学习因子做出异步调优改进,通过利用某路段的高速公路监测数据对改进的PSO-RBFNN算法进行训练,获得最优参数值,对短时交通流量进行预测,并将仿真结果与其他模型进行对比分析。实验结果表明,该文改进的PSO-RBFNN模型预测结果稳定,更适用于短时交通流量预测,具有更高的精度。
|
| 关 键 词： | 粒子群算法(PSO) 神经网络 径向基(RBF)神经网络 交通流量预测 |
The Improved PSO to Optimize the RBFNN Short-term Traffic Flow Prediction Method |
| |
| DONG Hai,YOU Ting,LI Xiao-chen.The Improved PSO to Optimize the RBFNN Short-term Traffic Flow Prediction Method[J].Digital Community & Smart Home,2021(5). |
| |
| Authors: | DONG Hai YOU Ting LI Xiao-chen |
| |
| Affiliation: | (Fujian Jiangxia University,Fuzhou 350108,China) |
| |
| Abstract: | Aiming at the prediction accuracy of short-term traffic flow,this paper uses PSO algorithm to optimize RBFNN model,and introduces learning factor optimization strategy to improve PSO algorithm to further improve the prediction accuracy.Aiming at the different roles of cognitive factor and social factor in the global search and local search of PSO algorithm,this paper improves the nonlinear learning factor asynchronously.By using the highway monitoring data of a certain section to train the improved PSORBFNN algorithm,the optimal parameters are obtained,and the short-term traffic flow is predicted.The simulation results are com?pared with other model analysis.The experimental results show that the improved PSO-RBFNN model is stable,more suitable for short-term traffic flow prediction,and has higher accuracy. |
| |
| Keywords: | particle swarm optimization neural network radial basis function traffic flow prediction |
| 本文献已被 维普 等数据库收录！ |
|
