Ultimate boundedness of stochastic Cohen-Grossberg neural networks with delays

Abstract

The ultimate boundedness is one of foundational concepts, which plays an important role in investigating the global asymptotic stability, its control and synchronization for dynamical systems. The ultimate boundedness of stochastic Cohen-Grossberg neural networks with time-varying delays is investigated. By employing Lyapunov method and matrix technique, some novel results and criteria on stochastic ultimate boundedness are derived. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results.