Quasi-synchronization of heterogeneous neural networks with hybrid time delays via sampled-data saturating impulsive control

This paper focuses on the quasi-synchronization matter of directed coupled heterogeneous neural networks with actuator saturation and hybrid time-varying delays under the sampled-data impulsive control. By utilizing the extended impulsive comparison principle and a dead-zone function representation of saturation nonlinearity function, multiple sufficient criteria are established for attaining the quasi-synchronization of heterogeneous dynamic neural networks. Meanwhile, the maximum estimating domain of attraction is acquired by solving an optimization problem which is confined by the linear matrix inequalities converted from the bilinear matrix inequalities. In addition, through utilizing the extended formula for the variation of parameters with hybrid delays, precise exponential convergence rate and theoretical error bound are calculated. Ultimately, the theoretical results are illustrated by means of a numerical simulation.