GABP神经网络在交通流预测中的应用研究

The Research of the Application of GABP Neural Network in Traffic Flow Prediction

交通流的预测是智能交通系统的重要技术之一,传统的神经网络预测方法存在对初始网络权值设置敏感、易陷入局部极小点、收敛速度慢等缺点。GABP神经网络是指在传统的BP神经网络的基础之上引入遗传算法,对神经网络的初始权值优化后再使用神经网络进行学习和训练。经本文验证,GABP网络不仅可以很好的避开传统BP网络的这些弊端,继承神经网络很强的学习、训练能力,同时也提高了网络的预测精度。

Traffic flow prediction is an key technology in the ITS （Intelligent Transport System）. However, there are lots of disadvantages in the traditional neural network prediction ways, including be sensitive to the initial network weights, easy to run into the local minimum point, etc. GABP network brings forward genetic algorithm to the BP neural network, optimizing the initial network weights. Proved by our research, not only the GABP network can avoid the disadvantages in the traditional BP network and inherit its good learning and training abilities, but also improve the prediction precision.