Analysis of Markovian Jump Stochastic Cohen–Grossberg BAM Neural Networks with Time Delays for Exponential Input-to-State Stability

Abstract

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