Global exponential stability for reaction–diffusion recurrent neural networks with multiple time-varying delays
Joint Authors
Source
The Arabian Journal for Science and Engineering. Section B, Engineering
Issue
Vol. 33, Issue 2B (31 Oct. 2008), pp.487-501, 15 p.
Publisher
King Fahd University of Petroleum and Minerals
Publication Date
2008-10-31
Country of Publication
Saudi Arabia
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Topics
Abstract AR
سوف نعرض في هذا البحث الثبات الأسي للشبكات العصبية ذات التأخر الزمني المتغير و المتعدد و حدود التفاعلية الانتشارية.
و يفترض أن اقترانات التنشيط تكون محدودة و متصلة كليا وفقا بـ (Lipschitz).
و قد حصلنا على الشروط الكافية باستخدام اقترانات (Lyapunov) التي تضمن الثبات الأسي الكلي للشبكة العصبية المعاقة.
و سوف نورد مثالا حسابيا لإظهار مدى صحة طريقة التحليل التي اتبعناها.
Abstract EN
In this paper, we consider the problem of exponential stability for recurrent neural networks with multiple time-varying delays and reaction–diffusion terms.
The activation functions are supposed to be bounded and globally Lipschitz continuous.
By means of Lyapunov functionals, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network.
Finally, a numerical example is given to show the correctness of our analysis.
American Psychological Association (APA)
Lou, Xuyang& Cui, Baotong. 2008. Global exponential stability for reaction–diffusion recurrent neural networks with multiple time-varying delays. The Arabian Journal for Science and Engineering. Section B, Engineering،Vol. 33, no. 2B, pp.487-501.
https://search.emarefa.net/detail/BIM-330186
Modern Language Association (MLA)
Lou, Xuyang& Cui, Baotong. Global exponential stability for reaction–diffusion recurrent neural networks with multiple time-varying delays. The Arabian Journal for Science and Engineering. Section B, Engineering Vol. 33, no. B2 (Oct. 2008), pp.487-501.
https://search.emarefa.net/detail/BIM-330186
American Medical Association (AMA)
Lou, Xuyang& Cui, Baotong. Global exponential stability for reaction–diffusion recurrent neural networks with multiple time-varying delays. The Arabian Journal for Science and Engineering. Section B, Engineering. 2008. Vol. 33, no. 2B, pp.487-501.
https://search.emarefa.net/detail/BIM-330186
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 500-501
Record ID
BIM-330186
