کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946747 | 1439416 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals
ترجمه فارسی عنوان
پایداری و پراکندگی جهانی در شبکه های عصبی تعمیم یافته با سیگنال های تاخیر زمانی متفاوت
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper investigates the problems of exponential stability and dissipativity of generalized neural networks (GNNs) with time-varying delay signals. By constructing a novel Lyapunov-Krasovskii functionals (LKFs) with triple integral terms that contain more advantages of the state vectors of the neural networks, and the upper bound on the time-varying delay signals are formulated. We employ a new integral inequality technique (IIT), free-matrix-based (FMB) integral inequality approach, and Wirtinger double integral inequality (WDII) technique together with the reciprocally convex combination (RCC) approach to bound the time derivative of the LKFs. An improved exponential stability and strictly (Q,S,R)-γ-dissipative conditions of the addressed systems are represented by the linear matrix inequalities (LMIs). Finally, four interesting numerical examples are developed to verify the usefulness of the proposed method with a practical application to a biological network.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 87, March 2017, Pages 149-159
Journal: Neural Networks - Volume 87, March 2017, Pages 149-159
نویسندگان
R. Manivannan, R. Samidurai, Jinde Cao, Ahmed Alsaedi, Fuad E. Alsaadi,