مدلسازی و شبیه‌سازی طراحی سیستم محموله ماهواره سنجش از دور برپایه سیستم استنتاج عصبی- فازی تطبیقی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسنده

عضو هیئت علمی، گروه مهندسی مکانیک، دانشگاه فنی و حرفه‌ای تهران، ایران.

چکیده

یکی از مهم‌ترین بخش‌ها در مدل‌سازی و شبیه‌سازی سیستم‌های فضایی، تعیین رفتار غیرخطی سیستم است. در این مقاله، استفاده مؤثر یکی از سیستم‌های هوش مصنوعی در مراحل اولیه فرایند طراحی سیستم‌ محموله ماهواره‌های سنجش از دور نشان داده می‌شود. مدل‌سازی و تحلیل سیستم‌های فضایی همواره با عدم‌قطعیت و عوامل غیرخطی در تشخیص و تصمیم‌گیری رفتار سیستم روبه‌رو است و شناخت این عومل می‌تواند به میزان مؤثربودن مدل‌سازی کمک شایانی کند. استفاده از روش استنتاج عصبی- فازی تطبیقی که داده‌های ذخیره شده در شبکه‌های آموزش‌دیده را به‌صورت یک قاعده فازی بیان می‌کند، به کاهش زمان مراحل اولیه طراحی و تعداد حلقه‌های تکراری تعاملات طراحی کمک می‌کند. در توسعه این متدولوژی از الگوریتم آموزش ترکیبی برای به‌دست‌آوردن پارامترهای سیستم استفاده می‌شود تا همگرایی سریع‌تر و کاهش ابعاد فضای جستجو ایجاد گردد. آموزش ترکیبی استفاده توأمان روش‌های حداقل مربعات و گرادیان نزولی است. همچنین سیستم استنتاج، برمبنای استنتاج تاکاگی- سوگینو و با توابع تعلق گائوسی مورداستفاده قرار می‌گیرد. مدل‌سازی حاضر بر روی سیستم محموله یک ماهواره اجرا می‌شود. نتایج شبیه‌سازی نشانگر اثربخشی سیستم استنتاج عصبی- فازی تطبیقی در طراحی مفهومی این سیستم است. میانگین خطای متغیرها برای چهار متغیر جرم، توان و حافظه روی‌برد محموله و میزان فشرده‌سازی در طراحی قابل قبول است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling and Simulation of Payload System Design in Remote Sensing Satellites Based on Adaptive Neuro-Fuzzy Inference System

نویسنده [English]

  • Morteza Ramezani
Faculty Member, Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran.
چکیده [English]

One of the main parts in modeling and simulation of space systems is to determine the nonlinear behavior of the system. In this article, it is shown how artificial intelligence systems could effectively be used in the early stages of a payload system design process in Remote Sensing Satellites (RSS). Modeling and simulation of space systems with nonlinearity and uncertainty in behavior recognition and decision making is vital. Time and cost of the conceptual design phase are decreased by using adaptive neuro-fuzzy approach, which enables the data that is stored in trained networks to be expressed in the form of a fuzzy rule base. In developing this methodology, a hybrid training algorithm was used to obtain system parameters achieving faster convergence and reduction in the size of the search space. This combined training used both least squares and descending gradient methods. The inference system was also based on the Takagi-Sugeno model with Gaussian membership functions. The present modeling system was implemented on the payload system of a satellite. The simulation results showed the effectiveness of the adaptive neuro-fuzzy inference system in the conceptual design of this system. The mean square error of the output variables for the four variables of mass, power and on-board memory of the payload and data compression in early stage of the design was acceptable.

کلیدواژه‌ها [English]

  • Satellite payload design Design time reduction Remote Sensing Satellite (RSS) System design Payload modeling and simulation Adaptive neuro
  • fuzzy inference system (ANFIS)
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