Traffic Flow Prediction Model Based on Deep Belief Network Optimized BP Neural Network

Abstract

Abstract:

In order to improve the prediction accuracy of BP neural network, this paper, based on deep learning theory, puts forward a deep belief network (DBN) algorithm to optimize the traditional BP neural network prediction. This algorithm is composed of multi Restricted Boltzmann Machine (RBM), and it uses unsupervised learning algorithm to train the parameters. Then, it uses reverse learning to fine tune the network parameters and optimize the threshold and weight of BP neural network. This way can derive the optimal solution through training. The experimental results show that the optimization model can overcome two shortcomings of traditional neural network:one is that traditional neural network tend to fall into local optimum and the other is that the function fitting degree remains low. Therefore, this model can effectively improve the prediction accuracy of traffic flow.

Key words: traffic flow prediction, deep learning, deep belief network, BP neural network, Restricted Boltzmann Machine

Kong Fanhui, Li Jian. Traffic Flow Prediction Model Based on Deep Belief Network Optimized BP Neural Network[J]. , 2020, 32(3): 300-306.

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