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

2013 ◽  
Vol 333-335 ◽  
pp. 1422-1425
Author(s):  
Ming Qiang Chen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Fuzzy Neural Network ◽  
Good Method ◽  
Air Traffic ◽  
Traffic Flow Prediction ◽  
Air Traffic Controllers ◽  
Flow Prediction ◽  
Air Traffic Flow ◽  
Fuzzy Neural

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.

Research of Air Traffic Flow Forecasts Based on BP Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2912-2915
Author(s):  
Ming Qiang Chen ◽  
Jun Hong Feng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Back Propagation ◽  
Good Method ◽  
Back Propagation Neural Network ◽  
Air Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
The Neural Network ◽  
Air Traffic Flow

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 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 back propagation neural network.

scholarly journals Research on the Air Traffic Flow Prediction Using a Deep Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 148019-148030
Author(s):  
Hong Liu ◽  
Yi Lin ◽  
Zhengmao Chen ◽  
Dongyue Guo ◽  
Jianwei Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Air Traffic ◽  
Learning Approach ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Air Traffic Flow

scholarly journals Air traffic prediction with fuzzy constraints

2021 ◽  
Author(s):  
N. Yamamoto ◽  
Zhou Shen ◽  
H. Yuan
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Air Traffic ◽  
Traffic Prediction ◽  
Fuzzy Constraints ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Air Traffic Flow ◽  
Fuzzy Constraint ◽  
Decision Vector

Abstract In view of the deficiencies of poor prediction accuracy, time-consuming and low efficiency of traditional traffic prediction models, fuzzy constraints are introduced into air traffic traffic system to represent some uncertain information in the field of artificial intelligence, and construct a fuzzy constraint-based air traffic flow prediction The fuzzy constraint-based air traffic prediction model is constructed. By analyzing the decision vector, fuzzy parameter vector and fuzzy constraint set, the prediction model is proposed. The air traffic flow prediction model is built by analyzing the decision vector, fuzzy parameter vector and fuzzy constraint set that affect the fuzzy constraint, and proposing the construction process of the prediction model. The experimental results show that the air traffic flow prediction model can be used to predict the air traffic flow. The experimental results show that the improved prediction model is better than the traditional prediction model in predicting air traffic flow. The results show that the improved prediction model has better prediction results, shorter time consumption and higher accuracy than the traditional prediction model.

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