基于改进BP神经网络探究航班与航空旅客关系

The Relationship between Flights and Air Passengers Based on Improved BP Neural Network

针对航班数量和航空旅客数量的定量关系难以准确求解的问题,提出了使用改进BP神经网络算法,训练并学习二者之间数学关系的方法.以航空旅客数量输出值和目标值之间的拟合系数均值反映网络的学习效果,从而确定隐含层最佳神经元的个数,减小随机误差对结果的影响,提高结果的准确度和可信度.基于首都国际机场的实际数据进行了仿真实验,结果表明,航空旅客数量的输出值与目标值的平均拟合系数在0.85以上,验证了本文所提出方法的有效性.

It is difficult to solve the quantitative relationship between the number of flights and air passengers accurately,and an approach is proposed to address the problem based on improved BP neural network algorithm.The average fitting coefficient between the output values and target values of air passengers is calculated to reflect the learning effect of the network and determine the optimal number of neurons in the hidden layer,which could reduce the impact of random errors on the results and improve the accuracy and reliability of the results.A simulation experiment is implemented based on the real data from the Capital International Airport and the results show that the fitting coefficient between the output values and target values of air passengers is over 0.87,which means the proposed method is effective.