Synchronization of Chaotic Neural Networks with Packet Dropouts and Uniform Quantization

This paper is concerned with the synchronization problem for chaotic neural networks under the networked environment. A novel synchronization model in the simultaneous presence of packet dropouts and uniform quantization is established. In virtue of the stochastic analysis approach and inequality technique, a sufficient condition is obtained under which the synchronization error is input-to-state stable in probability. Finally, a numerical example is presented to show the feasibility and effectiveness of the proposed results.