Global exponential synchronization of multiple coupled inertial memristive neural networks with time-varying delay via nonlinear coupling. Academic Article

abstract

• In this paper, global exponential synchronization of multiple coupled inertial memristive neural networks with time-varying delay is investigated. First, by choosing suitable variable substitution, the inertial memristive neural networks are transformed into first-order differential equations. Next, a novel coupling scheme with linear diffusive term and discontinuous sign function term depending on the first order derivative of state variables is introduced. Based on this coupling scheme, several sufficient conditions for global exponential synchronization of multiple inertial memristive neural networks are derived by using Lyapunov stability theory and some inequality techniques. Finally, several numerical examples are presented to substantiate the effectiveness of the theoretical results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Guo, Z., Gong, S., Yang, S., & Huang, T.

citation count

• 49

complete list of authors

• Guo, Zhenyuan||Gong, Shuqing||Yang, Shaofu||Huang, Tingwen

publication date

• December 2018

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Diffusion
• Global Exponential Synchronization
• Inertial Memristive Neural Network
• Neural Networks, Computer
• Nonlinear Coupling
• Nonlinear Dynamics
• Time Factors
• Time-varying Delay

PubMed Central ID

• 30243050

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2018.08.020

start page

• 260

end page

• 271

volume

• 108

URL

• http://dx.doi.org/10.1016/j.neunet.2018.08.020