改进粒子群算法优化 BP 神经网络的短时交通流预测

为提高 BP 神经网络预测模型的预测准确性, 提出了一种基于改进粒子群算法优化 BP 神经网络的预测方法. 引入自适应变异算子对陷入局部最优的粒子进行变异, 改进了粒子群算法的寻优性能, 利用改进粒子群算法优化 BP 神经网络的权值和阈值, 然后训练 BP 神经网络预测模型求得最优解. 将该预测方法应用到实测交通流的时间序列进行有效性验证, 结果表明了该方法对短时交通流具有更好的非线性拟合能力和更高的预测准确性.

Prediction for short-term traffic flow based on modified PSO optimized BP neural network

In order to improve forecasting model accuracy of BP neural network,an improved prediction method of optimized BP neural network based on modified particle swarm optimization algorithm(PSO) was proposed.In this modified PSO algorithm,an adaptive mutation operator was proposed in PSO to change positions of the particles which plunged in the local optimization.The modified PSO was used to optimize the weights and thresholds of BP neural network,and then BP neural network was trained to search for the optimal solution.The availability of the modified prediction method was proved by predicting the time series of real traffic flow.The computer simulations have shown that the nonlinear fitting and accuracy of the modified prediction methods are better than other prediction methods.