title: Synchronization control of memristor-based recurrent neural networks with perturbations.
abstract: In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.
authors: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
authors: Weiping
authors: W
authors: Wang
authors: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China. Electronic address: li_lixiang2006@163.com.
authors: Lixiang
authors: L
authors: Li
authors: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; Zhejiang Provincial Key Lab of Data Storage and Transmission Technology, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China.
authors: Haipeng
authors: H
authors: Peng
authors: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
authors: Jinghua
authors: J
authors: Xiao
authors: Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
authors: Yixian
authors: Y
authors: Yang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008568:Memory; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors
keywords: Boundary perturbation; Impulsive perturbation; Memristor-based recurrent neural networks; Synchronization control