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- W2034367117 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." @default.
- W2034367117 created "2016-06-24" @default.
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- W2034367117 date "2013-07-01" @default.
- W2034367117 modified "2023-09-27" @default.
- W2034367117 title "Using Hybrid Fuzzy Neural Network to Improve the Accuracy of Air Traffic Flow Forecasts" @default.
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- W2034367117 doi "https://doi.org/10.4028/www.scientific.net/amm.333-335.1422" @default.
- W2034367117 hasPublicationYear "2013" @default.
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