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Exponential Stability of Delayed Neural Networks with Impulses

This paper investigates the stability of a class of delayed neural networks with impulses. By means of Lyapunov direct method, the new sufficient conditions for exponential stability of the systems are derived.

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Main Authors:	Jin-Xiang Yang, Shou-Ming Zhong, Jian-Cheng Song
Format:	Conference Proceeding
Language:	English
Subjects:	Artificial neural networks Computer science Delay effects Differential equations Educational institutions Exponential stability Hopfield neural networks impulsive Neural networks Recurrent neural networks Stability criteria Sufficient conditions
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creator	Jin-Xiang Yang Shou-Ming Zhong Jian-Cheng Song
description	This paper investigates the stability of a class of delayed neural networks with impulses. By means of Lyapunov direct method, the new sufficient conditions for exponential stability of the systems are derived.
doi_str_mv	10.1109/ICACIA.2008.4770015
format	conference_proceeding
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ispartof	2008 International Conference on Apperceiving Computing and Intelligence Analysis, 2008, p.244-246
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Artificial neural networks Computer science Delay effects Differential equations Educational institutions Exponential stability Hopfield neural networks impulsive Neural networks Recurrent neural networks Stability criteria Sufficient conditions
title	Exponential Stability of Delayed Neural Networks with Impulses
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