Finite-time synchronization control of a class of memristor-based recurrent neural networks

Minghui Jiang; Shuangtao Wang; Jun Mei; Yanjun Shen

doi:10.1016/j.neunet.2014.11.005

Finite-time synchronization control of a class of memristor-based recurrent neural networks

Neural Netw. 2015 Mar:63:133-40. doi: 10.1016/j.neunet.2014.11.005. Epub 2014 Nov 27.

Authors

Minghui Jiang¹, Shuangtao Wang², Jun Mei³, Yanjun Shen⁴

Affiliations

¹ College of Science, China Three Gorges University, Yichang, Hubei 443002, China. Electronic address: jiangminghui@ctgu.edu.cn.
² College of Science, China Three Gorges University, Yichang, Hubei 443002, China. Electronic address: wangshuangtaojy@163.com.
³ Centre of New Energy Systems, Department of Electrical and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa. Electronic address: meij0000@163.com.
⁴ College of Science, China Three Gorges University, Yichang, Hubei 443002, China.

PMID: 25536233
DOI: 10.1016/j.neunet.2014.11.005

Abstract

This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method.

Keywords: Finite-time synchronization; Memristor; Recurrent neural networks.

Publication types

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

MeSH terms

Algorithms*
Neural Networks, Computer*
Nonlinear Dynamics
Time