In this article, the Input-to-state stability theory is used to investigate stochastic Cohen–Grossberg bidirectional associative memory neural network with time-varying delay. addition, Markovian jump parameters are considered in model determine continuous-time, discrete-state Markov chain. By utilizing Lyapunov functional and weak infinitesimal generator algebraic conditions derived for criter...

Abstract. Sufficient conditions are obtained for the existence and global exponential stability of periodic solution of high-order Cohen-Grossberg neural network with impulses by using Mawhin’s continuation theorem of coincidence degree and by means of a method based differential inequality.