改进的BP神经网络在交通流量预测中应用

针对传统BP学习算法收敛速度慢,对步长依赖明显等缺点,提出一种利用搜索较优步长的BP算法.在网络训练中,能够在每次迭代中搜索出一个相对合理的步长,从而使步长的选择对学习速度的影响大大降低.对交通流量预测仿真结果表明,新算法对步长选择的依赖性小于传统BP算法.

Application of Modified BP Neural Network in Traffic Flow Forecasts

Throughing the study the neural network can give a output whieh match the expectation output, if use it in traffic flow forecast may achieve the good effect. The traditional BP algorithm has some limitations such as the learning convergence speed is very slow and the learning process is dependent on the learning rate. In view of the limitations, this paper presents a BP neural network based on searching for more adaptive learning rate algorithm. The traffic flow fore-casts simulation results show that the new algorithm is less dependent on the learning rate than the traditional BP algo-rithm.