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A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers | ||
| Control and Optimization in Applied Mathematics | ||
| مقاله 6، دوره 3، شماره 1، مهر 2018، صفحه 87-108 اصل مقاله (611.68 K) | ||
| نوع مقاله: Applied Article | ||
| شناسه دیجیتال (DOI): 10.30473/coam.2019.40779.1083 | ||
| نویسندگان | ||
| Ghasem Ahmadi* 1؛ Mohammad Teshnehlab2؛ Fahimeh Soltanian3 | ||
| 1Department of Mathematics, Payame Noor University (PNU), P.O. Box 19395-3697, Tehran, Iran | ||
| 2Department of Control Engineering, K.N. Toosi University of Technology, Tehran, Iran | ||
| 3Department of Mathematics, Payame Noor University (PNU), P.O. Box, 19395-3697, Tehran, Iran | ||
| چکیده | ||
| o enhance the performances of rough-neural networks (R-NNs) in the system identification, on the base of emotional learning, a new stable learning algorithm is developed for them. This algorithm facilitates the error convergence by increasing the memory depth of R-NNs. To this end, an emotional signal as a linear combination of identification error and its differences is used to achieve the learning laws. In addition, the error convergence and the boundedness of predictions and parameters of the model are proved. To illustrate the efficiency of proposed algorithm, some nonlinear systems including the cement rotary kiln are identified using this method and the results are compared with some other models. | ||
| کلیدواژه‌ها | ||
| Rough-neural network؛ System identification؛ Emotional learning؛ Lyapunov stability theory | ||
| عنوان مقاله [English] | ||
| یادگیری عاطفی بر مبنای لیاپانوف بهنگام از مرتبه بالاتر برای شناساگرهای راف-عصبی | ||
| نویسندگان [English] | ||
| قاسم احمدی1؛ محمد تشنه لب2؛ فهیمه سلطانیان3 | ||
| 1استادیار، گروه ریاضی، دانشگاه پیام نور، صندوق پستی ۱۹۳۹۵-۳۶۹۷ ، تهران، ایران | ||
| 2گروه مهندسی کنترل، دانشگاه خواجه نصیرالدین طوسی | ||
| 3استادیار، گروه ریاضی، دانشگاه پیام نور، ص. پ. 3697-19395، تهران، ایران | ||
| چکیده [English] | ||
| به منظور بالا بردن کارایی شبکه‌های راف-عصبی در شناسایی سیستم، یک الگوریتم یادگیری پایدار بر مبنای یادگیری عاطفی برای آنها ارائه شده است. این الگوریتم با افزودن به عمق حافظه شبکه‌های راف-عصبی همگرایی خطا را آسان می‌کند. برای این منظور، از یک سیگنال عاطفی که ترکیبی خطی از خطای شناسایی و تفاضلات آن می‌باشد، برای دستیاب1ی به قوانین یادگیری استفاده شده است. علاوه بر این، همگرایی خطا و کرانداری پیش‌بینی‌ها و پارامترهای مدل اثبات شده است. برای نشان دادن کارآمدی الگوریتم پیشنهادی، چند سیستم غیرخطی شامل کوره دوار سیمان با استفاده از این روش شناسایی شده و نتایج با چند مدل دیگر مقایسه شده است. | ||
| کلیدواژه‌ها [English] | ||
| شبکه راف-عصبی, شناسایی سیستم, یادگیری عاطفی, نظریه پایداری لیاپانوف | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 539 تعداد دریافت فایل اصل مقاله: 351 |
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