Improved asymptotic stability conditions for neural networks with discrete and distributed delays

  • Authors:

  • Yonggang Chen;Shumin Fei;Kanjian Zhang

  • Affiliations:

  • Department of Mathematics, Henan Institute of Science and Technology, Xinxiang, 453003, China;Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, China;Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, China

  • Venue:
  • International Journal of Computer Mathematics
  • Year:
  • 2012

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Abstract

This paper considers the asymptotic stability problem for a class of neural networks with discrete and distributed delays. Based on a new augmented Lyapunov functional and integral inequalities, the new asymptotic stability condition is established in terms of linear matrix inequality. Meanwhile, the importance of some augmented terms in the Lyapunov functional are discussed. Compared with previous methods to deal with the distributed delay, our method is less conservative due to the use of the new Lyapunov functional. Finally, numerical examples illustrate the relaxation of obtained results and our claims.