Mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching based on vector Lyapunov functions.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Aug 24, 2016

Abstract

This paper studies the mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. By using the vector Lyapunov function and property of M-matrix, two generalized Halanay inequalities are established. By means of the generalized Halanay inequalities, sufficient conditions are also obtained, which can ensure the exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. Two numerical examples are given to illustrate the efficiency of the derived results.

Authors

  • Zhihong Li
    Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Lei Liu
    Department of Science and Technology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Quanxin Zhu
    School of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Nanjing, 210023, China; Department of Mathematics, University of Bielefeld, Bielefeld D-33615, Germany. Electronic address: zqx22@126.com.

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