Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks

Ying Wan; Jinde Cao; Guanghui Wen; Wenwu Yu

doi:10.1016/j.neunet.2015.10.009

Robust fixed-time synchronization of delayed Cohen-Grossberg neural networks

Neural Netw. 2016 Jan:73:86-94. doi: 10.1016/j.neunet.2015.10.009. Epub 2015 Oct 26.

Authors

Ying Wan¹, Jinde Cao², Guanghui Wen¹, Wenwu Yu¹

Affiliations

¹ Department of Mathematics, Southeast University, Nanjing 210096, China.
² Department of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: jdcao@seu.edu.cn.

PMID: 26575975
DOI: 10.1016/j.neunet.2015.10.009

Abstract

The fixed-time master-slave synchronization of Cohen-Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results.

Keywords: Cohen–Grossberg neural network; Discontinuous control; Finite-time synchronization; Fixed-time synchronization; Time-varying delay.

Publication types

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

MeSH terms

Algorithms
Models, Statistical
Neural Networks, Computer*
Time Factors
Uncertainty