Research of Air Traffic Flow Forecasts Based on BP Neural Network
| Autor: | Jun Hong Feng, Ming Qiang Chen |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Engineering
Traffic congestion reconstruction with Kerner's three-phase theory Artificial neural network business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Real-time computing General Engineering Training (meteorology) ComputerApplications_COMPUTERSINOTHERSYSTEMS Air traffic control Traffic flow Network traffic simulation Flow (mathematics) ComputerSystemsOrganization_MISCELLANEOUS business Traffic generation model Simulation |
| Zdroj: | Advanced Materials Research. :2912-2915 |
| ISSN: | 1662-8985 |
| DOI: | 10.4028/www.scientific.net/amr.671-674.2912 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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