计算机应用与软件2013，Vol.30Issue(6)：225-227,242,4.DOI:10.3969/j.issn.1000-386x.2013.06.059
混沌粒子群优化神经网络在铁路客运量预测中的应用
APPLICATION OF CPS OPTIMISED NEURAL NETWORK IN RAILWAY PASSENGER TRAFFIC PREDICTION
赵清艳 1熊茂华2
作者信息
- 1. 中山职业技术学院 广东中山528404
- 2. 广州番禺职业技术学院 广东广州511483
- 折叠
摘要
Abstract
Conventional RBF neural network has the defects of slow convergence,easy to fall into local minimum and so on in railway passenger traffic prediction.In light of this,we propose to optimise the parameters of the RBF neural networks based on chaotic particle swarm (CPS),and carry out the simulation experiment on the railway passenger traffic of China during 1985-2008.Simulation results show that the proposed algorithm is a good solution for parameter optimisation problem of conventional RBF neural networks,and improves the prediction accuracy of railway passenger traffic.The prediction results has more practical reference value for the decision-making of railway enterprises.关键词
铁路客运量/神经网络/预测/混沌粒子群算法Key words
Railway passenger traffic / Neural network / Prediction /CPS分类
信息技术与安全科学引用本文复制引用
赵清艳,熊茂华..混沌粒子群优化神经网络在铁路客运量预测中的应用[J].计算机应用与软件,2013,30(6):225-227,242,4.
