New synchronization criteria for memristor-based recurrent neural networks with mixed delays
Xiangxiang Wang, Yongbin Yu, Nijing Yang, Huihui Ma, Chenyu Yang, Shouming Zhong, Nyima Tashi · 2017
This paper presents global exponential synchronization of drive-response memristor-based recurrent neural networks(MRNNs) with mixed delays. Considering the bounded distributed delays and time-varying delays as a novel kind of mixed delays, the drive-response MRNNs with mixed delays are proposed. Then, a state feedback controller is designed. By using a novel lyapunov functional and inequality techniques, novel global exponential synchronization criteria on MRNNs with mixed delays are derived. Finally, a numerical example with simulation results is given to illustrate our theoretical results.