Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data

IEEE Trans Cybern. 2013 Dec;43(6):1796-806. doi: 10.1109/TSMCB.2012.2230441.

Abstract

In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computer Simulation
  • Markov Chains*
  • Models, Statistical*
  • Neural Networks, Computer*
  • Sample Size
  • Signal Processing, Computer-Assisted*
  • Stochastic Processes*