改进的粒子群算法优化TSFNN的交通流预测

为提高T-S模糊神经网络在交通流量预测的准确性,提出了一种改进的粒子群算法优化T-S模糊神经网络预测交通流量的算法。该算法利用改进粒子群算法通过群体极值进行t分布变异,使算法跳出局部收敛,使用改进的粒子群算法优化T-S模糊神经网络,能够优化网络参数配置,进而提高网络的预测精度。利用优化后的T-S模糊神经网络对实测交通流量进行预测,实验仿真表明优化的T-S模糊神经网络可有效提高交通流量预测精度,减小预测误差。

Traffic flow prediction based on T-S fuzzy neural network optimized improved particle swarm optimization

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.