Abstract: In recent years, deep learning has become the main motive of innovative solutions to artificial intelligence problems, which is made possible by increasing the amount of data available, increasing computing resources, and improving techniques in deep network training. The development and increase of computer processing power and the empowerment of artificial intelligence techniques such as machine learning and deep learning have made it easier for many aerospace projects to be implemented. Theoretical and biological arguments show that in order to build an intelligent system with the ability to extract high-level and powerful representations from data, models with deep architecture that include many nonlinear processing layers are needed. Arguably, the best and most widely used examples of these networks are multilayer neural networks due to their compatibility with data types. Deep neural networks have different structures, different types, and species, and they are used according to the type of data and the purpose of the problem, and each has its strengths and weaknesses. In this article, the study and application of these networks in various aerospace issues are discussed.
Raouf Moghadam, M., & Ebrahimi, M. (2023). Application of deep learning in aerospace industry. Soft Computing Journal, (), -. doi: 10.22052/scj.2023.243422.1036
MLA
Mahla Raouf Moghadam; Masoud Ebrahimi. "Application of deep learning in aerospace industry". Soft Computing Journal, , , 2023, -. doi: 10.22052/scj.2023.243422.1036
HARVARD
Raouf Moghadam, M., Ebrahimi, M. (2023). 'Application of deep learning in aerospace industry', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2023.243422.1036
VANCOUVER
Raouf Moghadam, M., Ebrahimi, M. Application of deep learning in aerospace industry. Soft Computing Journal, 2023; (): -. doi: 10.22052/scj.2023.243422.1036
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