Using Hybrid Fuzzy Neural Network to Improve the Accuracy of Air Traffic Flow Forecasts

Abstract:

Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the hybrid fuzzy neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by hybrid fuzzy neural network.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1422-1425
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1422
• Online since: July 2013
• Authors: Ming Qiang Chen
• Keywords: Airport Traffic Flow, Hybrid Fuzzy Neural Networks, Prediction Models

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