基于遗传算法(GA)优化BP神经网络的船舶交通流量预测

Ship Traffic Flow Prediction Based on Genetic Algorithm to Optimize BP Neural Network

为了提高船舶交通流量的预测精度,在BP神经网络的基础上,结合遗传算法(GA)建立一个新的预测模型.该模型利用GA自适应搜索能力和较快的收敛速度,进而确定BP神经网络中的最优权值和阈值.以青岛港2011—2019年船舶交通流量统计数据为例,进行仿真实例验证.结果表明,与传统的BP神经网络相比,该模型能显著地提高船舶交通流量的预测精度,用于预测船舶交通流量具有一定可行性.

In order to improve the prediction accuracy of ship traffic flow,a new prediction model was established based on BP neural network and genetic algorithm(GA).This model uses GA's adaptive search capability and fast convergence speed to determine the optimal weights and thresholds in the BP neural network.Take the statistics of Qingdao Port's ship traffic flow from 2011 to 2019 as an example,and verify the simulation examples.The results show that compared with the traditional BP neural network,the model can significantly improve the prediction accuracy of ship traffic flow,and it is feasible to predict ship traffic flow.