Guaranteed cost synchronization of discrete-time chaotic neural networks with missing measurements and randomly occurring sensor nonlinearity

This paper investigates the guaranteed cost synchronized problem of discrete-time chaotic neural networks under network environment by employing an output feedback control law. Both randomly occurring sensor nonlinearity and missing measurements are taken into account to better reflect the reality of network environment. Meanwhile, a novel model is proposed to account for these two phenomena by using a Kronecker delta function. Different from the existing related work, Bernoulli distributed white sequences is abandoned in this paper. In this situation, a guaranteed cost output feedback control law is established such that the synchronization error system reaches asymptotic stability with a guaranteed cost value. Finally, the effectiveness of the proposed method is verified via a simulation example.