Mean square stabilization and mean square exponential stabilization of stochastic BAM neural networks with Markovian jumping parameters

• This paper introduces a non-conservative Lyapunov functional.
• The achieved results impose non-conservative and can be widely used.
• The conditions are easily checked by the Matlab LMI Tool Box. The desired state feedback controller can be well represented by the conditions.

This paper addresses the mean square exponential stabilization problem of stochastic bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time-varying delays. By establishing a proper Lyapunov–Krasovskii functional and combining with LMIs technique, several sufficient conditions are derived for ensuring exponential stabilization in the mean square sense of such stochastic BAM neural networks. In addition, the achieved results are not difficult to verify for determining the mean square exponential stabilization of delayed BAM neural networks with Markovian jumping parameters and impose less restrictive and less conservative than the ones in previous papers. Finally, numerical results are given to show the effectiveness and applicability of the achieved results.