CS算法优化BP神经网络的短时交通流量预测

为了提高短时交通流量的预测精度，提出一种布谷鸟搜索算法优化BP神经网络参数的短时交通流量预测模型（CS-BPNN）。基于混沌理论对短时交通流量时间序列进行相空间重构，将重构后的时间序列输入到BP神经网络进行学习，采用布谷鸟搜索算法找到BP神经网络最优参数，建立短时交通流量预测模型，通过具体实例对CS-BPNN性能进行测试。仿真结果表明，相对于对比模型，CS-BPNN提高了短时交通流量的预测精度，更加准确反映了短时交通流量的变化趋势。

Short time traffic flow prediction model based on neural network and cuckoo search algorithm

In order to improve the prediction accuracy of short time traffic flow，this paper proposes a network traffic prediction model based on Cuckoo Search algorithm and BP Neural Network（CS-BPNN）.The time series of short time traffic flow is reconstructed to form a multidimensional time series based on chaotic theory，and then the time series are input into BP neural network to learn which parameters of BP neural network are optimized by cuckoo search algorithm to find the optimal parameters and establish the short time traffic flow prediction model.The performance of CS-BPNN is tested by the simulation experiments.The simulation results show that the proposed model improves the prediction accuracy of short time traffic flow and can more describe network traffic complex trend compared with reference models.