Transactions of Nanjing University of Aeronautics & Astronautics
Airport Aviation Noise Prediction Based on an Optimized Neural Network
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Abstract:
In order to alleviate noise pollution and improve the sustainability of airport operation， it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests， the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction， contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.
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Project Supported:
The work was supported by the National Natural Science Foundation of China （No. 61671237）， the Foundation of State Key Laboratory of Air Traffic Management System and Technology （No. SKLATM202003）， and the Fundamental Research Funds for Graduates of Nanjing University of Aeronautics and Astronautics （No. kfjj20200735）.
Clc Number:
X82
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