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Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays

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  • Sriraman, R.
  • Cao, Yang
  • Samidurai, R.

Abstract

This paper studies the global asymptotic stability problem for a class of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays as well as stochastic disturbances. Based on the Lyapunov–Krasovskii functional (LKF) method and mathematical analytic techniques, delay-dependent stability criteria are derived by separating complex-valued neural networks (CVNNs) into real and imaginary parts. Furthermore, the obtained sufficient conditions are presented in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two simulation examples are provided to show the effectiveness and advantages of the proposed results.

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  • Sriraman, R. & Cao, Yang & Samidurai, R., 2020. "Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 103-118.
    • Handle: RePEc:eee:matcom:v:171:y:2020:i:c:p:103-118
    DOI: 10.1016/j.matcom.2019.04.001
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