Quasi-Synchronization for Periodic Neural Networks With Asynchronous Target and Constrained Information

This paper investigates the problem of quasi-synchronization (QS) for the periodic neural networks (NNs). In order to address more general NNs, the parameter and the period mismatches are both considered, that is, excluding parameters, periods of the target dynamic and the followers are also different. In addition, the constrainted target information is studied, where the logarithmic quantizer is used to overcome the limited communication capacity and Bernoulli processes are employed to model cases of the information loss. A new period is established based on the lowest common multiple period of the target dynamic and the followers to obtain an augmented synchronization error system (ASES). Further, a suboptimal iterative algorithm is proposed to cut down the QS range of the ASES and the corresponding controllers are designed. At last, the controller design method is illustrated by a numerical example.