Stability Analysis for Delayed Neural Networks via a Novel Negative-Definiteness Determination Method.

Journal: IEEE transactions on cybernetics
Published Date:

Abstract

The stability of neural networks with a time-varying delay is studied in this article. First, a relaxed Lyapunov-Krasovskii functional (LKF) is presented, in which the positive-definiteness requirement of the augmented quadratic term and the delay-product-type terms are set free, and two double integral states are augmented into the single integral terms at the same time. Second, a new negative-definiteness determination method is put forward for quadratic functions by utilizing Taylor's formula and the interval-decomposition approach. This method encompasses the previous negative-definiteness determination approaches and has less conservatism. Finally, the proposed LKF and the negative-definiteness determination method are applied to the stability analysis of neural networks with a time-varying delay, whose advantages are shown by two numerical examples.

Authors

  • Fei Long
    Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA.
  • Chuan-Ke Zhang
    School of Automation, China University of Geosciences, Wuhan 430074, China; Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK. Electronic address: ckzhang@cug.edu.cn.
  • Yong He
    College of Biosystems Engineering and Food Science, Zhejiang Univ., Hangzhou, 310058, China.
  • Qing-Guo Wang
  • Min Wu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.