کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6863131 | 1439405 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations
ترجمه فارسی عنوان
شبکه عصبی غیرخطی مجدد برای راه حل محدود زمان معادلات خطی ماتریس متغیر زمان
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کلمات کلیدی
شبکه های عصبی مجدد غیرخطی، معادلات ماتریس خطی متغیر زمان معین، همگام سازی در زمان محدود، توابع فعال غیرخطی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 98, February 2018, Pages 102-113
Journal: Neural Networks - Volume 98, February 2018, Pages 102-113
نویسندگان
Lin Xiao, Bolin Liao, Shuai Li, Ke Chen,