Anti-synchronization for Markovian neural networks via asynchronous intermittent control

This paper concerns the anti-synchronization for master–slave Markovian neural networks (NNs), in which a more general Markovian chain with uncertain transition probability is introduced to the controller of slave NNs. An asynchronous intermittent control scheme is proposed to achieve the anti-synchronization, which not only overcomes the problem that the jumping information of the system mode is not available for the controller instantly, but also strikes a balance between control cost and accuracy. Then, sufficient conditions are established to guarantee the anti-synchronization of the master–slave Markovian NNs according to the Lyapunov stability method and the iteration method. Finally, the effectiveness of the proposed asynchronous intermittent control method is verified by means of simulation examples.