计算机工程与应用Issue(4)：236-239,4.DOI:10.3778/j.issn.1002-8331.1309-0478
改进的粒子群算法优化TSFNN的交通流预测
Traffic flow prediction based on T-S fuzzy neural network optimized improved particle swarm optimization
摘要
Abstract
In order to improve the accuracy of traffic flow prediction, a prediction algorithm for traffic flow of T-S fuzzy neural network optimized Improved Particle Swarm Optimization(IPSOTSFNN)is proposed. In the algorithm, Improved Particle Swarm Optimization(IPSO)is used to make the algorithm to jump out of local convergence by using t distribution. IPSO is used to optimize T-S Fuzzy Neural Network(TSFNN), it can improve the network parameters configuration, and then improve the prediction accuracy of the network. The efficiency of the proposed prediction method is tested by the simulation of real traffic flow, the simulation results show that the proposed method can effectively improve the prediction precision and reduce the traffic prediction error in traffic flow prediction.关键词
粒子群算法/T-S模型/模糊神经网络/交通流量预测Key words
Particle Swarm Optimization(PSO)/T-S model/fuzzy neural network/traffic flow prediction分类
信息技术与安全科学引用本文复制引用
侯越,赵贺..改进的粒子群算法优化TSFNN的交通流预测[J].计算机工程与应用,2014,(4):236-239,4.基金项目
兰州交通大学青年科学基金项目（No.2013006）。 （）
