Global exponential stability of a class of memristive neural networks with time-varying delays Academic Article

abstract

• This paper studies the uniqueness and global exponential stability of the equilibrium point for memristor-based recurrent neural networks with time-varying delays. By employing Lyapunov functional and theory of differential equations with discontinuous right-hand side, we establish several sufficient conditions for exponential stability of the equilibrium point. In comparison with the existing results, the proposed stability conditions are milder and more general, and can be applied to the memristor-based neural networks model whose connection weight changes continuously. Numerical examples are also presented to show the effectiveness of the theoretical results. 2013 Springer-Verlag London.

authors

• Huang, Tingwen

published proceedings

• NEURAL COMPUTING & APPLICATIONS

author list (cited authors)

• Wang, X., Li, C., Huang, T., & Duan, S.

citation count

• 50

complete list of authors

• Wang, Xin||Li, Chuandong||Huang, Tingwen||Duan, Shukai

publication date

• June 2014

publisher

• Springer Nature  Publisher

published in

• Neural Computing and Applications  Journal

keywords

• Exponential Stability
• Lyapunov Functional
• Memristive Neural Network
• Time Delay

Digital Object Identifier (DOI)

• 10.1007/s00521-013-1383-1

start page

• 1707

end page

• 1715

volume

• 24

issue

• 7-8

URL

• http://dx.doi.org/10.1007/s00521-013-1383-1