Exponential synchronization of Markovian jumping neural networks with partly unknown transition probabilities via stochastic sampled-data control

A. Chandrasekar, R. Rakkiyappan, Fathalla A. Rihan, S. Lakshmanan

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

Abstract

This paper investigates the exponential synchronization for a class of delayed neural networks with Markovian jumping parameters and time varying delays. The considered transition probabilities are assumed to be partially unknown. In addition, the sampling period is assumed to be time-varying that switches between two different values in a random way with given probability. Several delay-dependent synchronization criteria have been derived to guarantee the exponential stability of the error systems and the master systems are stochastically synchronized with the slave systems. By introducing an improved Lyapunov-Krasovskii functional (LKF) including new triple integral terms, free-weighting matrices, partly unknown transition probabilities and combining both the convex combination technique and reciprocal convex technique, a delay-dependent exponential stability criteria is obtained in terms of linear matrix inequalities (LMIs). The information about the lower bound of the discrete time-varying delay is fully used in the LKF. Furthermore, the desired sampled-data synchronization controllers can be solved in terms of the solution to LMIs. Finally, numerical examples are provided to demonstrate the feasibility of the proposed estimation schemes from its gain matrices.

Original languageEnglish
Pages (from-to)385-398
Number of pages14
JournalNeurocomputing
Volume133
DOIs
Publication statusPublished - Jun 10 2014

Keywords

  • Combined convex technique
  • Exponential synchronization
  • Markov jump systems
  • Sampled-data control
  • Stochastic sampling

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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