Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article µ-Stability of Impulsive Neural Networks with Unbounded Time-Varying Delays and Continuously Distributed Delays | Hindawi Publishing Corporation Advances in Difference Equations Volume 2011 Article ID 437842 12 pages doi 2011 437842 Research Article -Stability of Impulsive Neural Networks with Unbounded Time-Varying Delays and Continuously Distributed Delays Lizi Yin1 2 and Xilin Fu3 1 School of Management and Economics Shandong Normal University Jinan 250014 China 2 School of Science University of Jinan Jinan 250022 China 3 School of Mathematical Sciences Shandong Normal University Jinan 250014 China Correspondence should be addressed to Lizi Yin ss_yinlz@ Received 13 November 2010 Revised 19 February 2011 Accepted 3 March 2011 Academic Editor Jin Liang Copyright 2011 L. Yin and X. Fu. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper is concerned with the problem of -stability of impulsive neural systems with unbounded time-varying delays and continuously distributed delays. Some -stability criteria are derived by using the Lyapunov-Krasovskii functional method. Those criteria are expressed in the form of linear matrix inequalities LMIs and they can easily be checked. A numerical example is provided to demonstrate the effectiveness of the obtained results. 1. Introduction In recent years the dynamics of neural networks have been extensively studied because of their application in many areas such as associative memory pattern recognition and optimization 1-4 . Many researchers have a lot of contributions to these subjects. Stability is a basic knowledge for dynamical systems and is useful to the real-life systems. The time delays happen frequently in various engineering biological and economical systems and they may cause instability and poor performance of practical systems. Therefore the stability analysis for neural networks with time-delay has attracted a large amount of research .
