کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
473058 | 698765 | 2012 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Exponential H∞H∞ stable learning method for Takagi–Sugeno fuzzy delayed neural networks: A convex optimization approach
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper, we propose some new results on stability for Takagi–Sugeno fuzzy delayed neural networks with a stable learning method. Based on the Lyapunov–Krasovskii approach, for the first time, a new learning method is presented to not only guarantee the exponential stability of Takagi–Sugeno fuzzy neural networks with time-delay, but also reduce the effect of external disturbance to a prescribed attenuation level. The proposed learning method can be obtained by solving a convex optimization problem which is represented in terms of a set of linear matrix inequalities (LMIs). An illustrative example is given to demonstrate the effectiveness of the proposed learning method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Mathematics with Applications - Volume 63, Issue 5, March 2012, Pages 887–895
Journal: Computers & Mathematics with Applications - Volume 63, Issue 5, March 2012, Pages 887–895
نویسندگان
Choon Ki Ahn,