基于布谷鸟算法的BP神经网络短时交通流预测

Short-term traffic flow prediction based on BP neural network via Cuckoo optimization

利用人工神经网络的并行处理自适应学习能力可实现车流量预测,但网络权值的训练因搜索方法单一容易陷入局部极小值,不易得到全局最优解。将布谷鸟算法用于网络权值矩阵的训练,借助其搜索速度快、不易陷入局部最优解的优势,提出改进的网络学习方案。基于MATLAB软件对所得网络进行训练并将结果用于短时交通流实验,结果表明所得模型的预测结果误差小且具有收敛速度快、容易得到全局最优解的能力。

The parallel processing adaptive learning ability of artificial neural network can be used to predict the traffic flow.However,the training of network weights is easy to fall into local minimum because of the single search method,and it is difficult to get the global optimal solution.The algorithm of Cuckoo is applied to the training of network weight matrix.With the advantage of fast searching speed and being not easy to fall into local optimum,an improved network learning scheme is proposed.MATLAB software is used to train the network and apply the results to the experimental study of short-term traffic flow.The results show that the prediction error of the model is small,and it has the ability of fast convergence and is easy to get the global optimal solution.