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Stable Rough Extreme Learning Machines for the Identification of Uncertain Continuous-Time Nonlinear Systems | ||
| Control and Optimization in Applied Mathematics | ||
| دوره 4، شماره 1، مهر 2019، صفحه 83-101 اصل مقاله (469.62 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.30473/coam.2020.51511.1137 | ||
| نویسنده | ||
| Ghasem Ahmadi* | ||
| Payame Noor University (PNU), Tehran, Iran | ||
| چکیده | ||
| Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated. In this paper, we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties, and we prove the global asymptotically convergence of the proposed learning algorithm using the Lyapunov stability theory. Then, we use the proposed methodology to identify the chaotic systems of Duffing's oscillator and Lorentz system. Simulation results show the efficiency of the proposed model. | ||
| کلیدواژه‌ها | ||
| System identification؛ Extreme learning machine؛ Rough-neural network؛ Rough extreme learning machine؛ Lyapunov stability theory | ||
| عنوان مقاله [English] | ||
| ماشینهای یادگیری حدی راف پایدار برای شناسایی سیستمهای غیرخطی زمان-پیوسته غیرقطعی | ||
| نویسندگان [English] | ||
| قاسم احمدی | ||
| دانشگاه پیام نور، تهران، ایران | ||
| چکیده [English] | ||
| ماشینهای یادگیری حدی راف شبکه‌های راف-عصبی با یک لایه پنهان هستند که در آنها پارامترهای بین ورودی‌ها و نورون‌های پنهان به صورت تصادفی انتخاب می‌شوند و هرگز به روز نمی‌شوند. در این مقاله، ماشینهای یادگیری حدی راف با یک الگوریتم یادگیری برخط پایدار را برای شناسایی سیستمهای غیرخطی زمان-پیوسته در حضور نویزها و عدم‌قطعیت‌ها پیشنهاد می‌کنیم و با استفاده از نظریه پایداری لیاپانوف، همگرایی مجانبی سراسری الگوریتم یادگیری پیشنهاد شده را اثبات می‌کنیم. سپس، از روش پیشنهاد شده برای شناسایی سیستمهای آشوبی نوسانگر دافینگ و سیستم لورنز بهره می‌گیریم. نتایج شبیه‌سازی کارآمدی مدل پیشنهادی را نشان می‌دهد. | ||
| کلیدواژه‌ها [English] | ||
| شناسایی سیستم, ماشین یادگیری حدی, شبکه راف-عصبی, ماشین یادگیری حدی راف, نظریه پایداری لیاپانوف | ||
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