مروری بر رویکرد یادگیری عمیق در صنعت هوافضا

نوع مقاله : مقاله ترویجی

نویسندگان

گروه هوافضا، دانشکده مهندسی مکانیک، دانشگاه تربیت مدرس، تهران، ایران

چکیده

در سال‌های اخیر، یادگیری عمیق به محرک اصلی راه‌حل‌های نوآورانه برای مساله‌های هوش مصنوعی تبدیل گردیده که این امر با افزایش مقدار داده‌های موجود، افزایش منابع محاسباتی و روش‌های بهبود یافته در آموزش شبکه‌های عمیق امکان‌پذیر شده است. پیشرفت و افزایش توان پردازش رایانه‌ها و توانمندتر شدن روش‌های هوش مصنوعی مانند یادگیری ماشین و یادگیری عمیق، زمینه را برای اجرا شدن بسیاری از طرح‌های هوافضایی آسان‌تر نموده است. استدلال‌های نظری و بیولوژیکی نشان می‌دهند که در راستای ساخت سیستمی هوشمند با توانایی استخراج بازنمایی‌های سطح بالا و قدرتمند از داده‌ها، نیاز به مدل‌هایی با معماری عمیقی است که شامل بسیاری از لایه‌های پردازشی غیرخطی می‌باشد. شاید بتوان گفت، بهترین و پرکاربردترین نمونه از این شبکه‌ها، به دلیل سازگاری آن با انواع داده‌ها، شبکه‌های عصبی چندلایه هستند. شبکه‌های عصبی عمیق ساختارهای متفاوت، انواع مختلف و گونه‌های متنوعی را دارا هستند و با توجه به نوع داده‌ها و هدف مساله از آنها استفاده می‌شود و هرکدام دارای نقاط قوت و ضعف خود را دارند. در این مقاله به بررسی و کاربرد این شبکه‌ها در مساله‌های مختلف هوافضایی پرداخته شده است.

کلیدواژه‌ها


عنوان مقاله [English]

An Overview of Deep Learning Applications in the Aerospace Industry

نویسندگان [English]

  • Mahla Raouf Moghadam
  • Masoud Ebrahimi
Aerospace Department, Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

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 paper, the study and application of these networks in various aerospace issues are discussed.

کلیدواژه‌ها [English]

  • Deep learning
  • Artificial neural network
  • Deep network
  • Transfer learning
  • Geometric learning
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