Synchronization control of memristor-based recurrent neural networks with perturbations

Weiping Wang; Lixiang Li; Haipeng Peng; Jinghua Xiao; Yixian Yang

doi:10.1016/j.neunet.2014.01.010

Synchronization control of memristor-based recurrent neural networks with perturbations

Neural Netw. 2014 May:53:8-14. doi: 10.1016/j.neunet.2014.01.010. Epub 2014 Jan 28.

Authors

Weiping Wang¹, Lixiang Li², Haipeng Peng³, Jinghua Xiao¹, Yixian Yang⁴

Affiliations

¹ School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
² 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.
³ 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.
⁴ Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.

PMID: 24524891
DOI: 10.1016/j.neunet.2014.01.010

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.

Keywords: Boundary perturbation; Impulsive perturbation; Memristor-based recurrent neural networks; Synchronization control.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computer Simulation
Humans
Memory
Neural Networks, Computer*
Nonlinear Dynamics
Time Factors