ارائه اپراتور جدید جایگزین پخش قطره جوهر در روش یادگیری فعال

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی برق - دانشگاه صنعتی شریف

2 دانشکده فنی و مهندسی - دانشگاه مازندران

چکیده

روش یادگیری فعال یکی از روش‌های یادگیری فازی است که  الگو­گرفته از پردازش در مغز انسان است. اپراتور پخش قطره جوهر، موتور اصلی پردازشی در این روش است که به دور از فرمول­های پیچیده، به­دنبال یافتن رابطه بین خروجی و هر یک از ورودی‌ها است. زیاد­بودن حافظه مورد نیاز برای پیاده‌سازی صفحات پخش قطره جوهر و همچنین حجم محاسبات زیاد لازم برای استخراج ویژگی‌ها از جمله مشکلات پیش روی اپراتور پخش قطره جوهر است. در این مقاله یک روش جایگزین برای اپراتور پخش قطره جوهر ارائه شده است که سبب کاهش چشم‌گیر پیچیدگی محاسباتی می‌شود. الگوریتم ارائه­شده با استفاده از دو بردار حافظه به توصیف صفحات پخش جوهر می‌پردازد که مشکل اتلاف زیاد حافظه را حل می‌کند. الگوریتم پیشنهادی، ویژگی‌های مسیر باریک و پراکندگی داده در صفحات پخش قطره جوهر را که مهم‌ترین مفاهیم برای استفاده در مرحله استنتاج الگوریتم یادگیری فعال هستند را با صرف کمترین هزینه و زمان محاسباتی می‌یابد. برای بررسی صحت عملکرد الگوریتم، شبیه‌سازی‌هایی بر روی مجموعه داده‌های استاندارد در حوزه مدل‌سازی و طبقه‌بندی ارائه شده است. زمان و دقت  الگوریتم پیشنهادی با روش یادگیری فعال، شبکه‌های عصبی چندلایه پرسپترون و سیستم استنتاج عصبی-فازی تطبیق‌پذیر مقایسه شده است.

کلیدواژه‌ها

عنوان مقاله [English]

A Novel Alternative Operator for Ink Drop Spread (IDS) in Active Learning Method (ALM)

نویسندگان [English]

  • S. Haghzad Klidbary 1
  • S. Bagheri Shouraki 1
  • I. Esmaili Paeen Afrakoti 2
1 Faculty of Electrical Engineering, Sharif University of Technology, Tehran, Iran
2 Departments of Engineering and Technology, University of Mazandaran, Babolsar, Iran
چکیده [English]

Active Learning method is one of the fuzzy learning methods inspired from the computation of human brain. Ink drop spread operator is the main computational engine in ALM which without using any complex formula, seeks for the relationship between the output and the inputs of the system. One of the challenges of IDS operator is that not only a large memory is required for implementing IDS planes, but also extracting features imposes high computational costs. In this paper, one learning method, as a replacement for IDS operator, is represented that considerable reduces the computational complexity. The represented algorithm defines the IDS planes with only two memory vectors and solves the problem of huge wastage of memory in these planes. This algorithm starts to learn the available patterns in learning data to find two features of Narrow p < /em>ath and Spread in planes which are the most important concepts in the level of active inference learning with minimum computational and time cost. To investigate the accuracy of algorithm’s performance, some simulations in modelling and classification have been done on standard data sets. Time and accuracy of proposed algorithm is compared with traditional ALM, MLP and ANFIS algorithms.

کلیدواژه‌ها [English]

  • Brain learning simulation
  • active learning method
  • ink drop spread operator
  • fuzzy inference system
  • pattern classification

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