Global exponential stability of memristive Cohen-Grossberg neural networks with mixed delays and impulse time window

abstract

• 2017 Elsevier B.V. This paper addresses the problem of global exponential stability for a class of memristive CohenGrossberg with mixed time delays and impulsive time window. Based on the memristor theory, impulse control theory, and mathematical induction method, stability criteria for the considered memristive CohenGrossberg neural networks are derived by employing appropriate Lyapunov functions. Compared with existing impulse control schemes, the impulse instants are extended to some bounded time intervals, we will show that the neural networks can still be stable under reasonable assumptions. Furthermore, the conditions obtained in this paper are easy to be checked, and they can be applied to improve previous results concerning stability for memristive neural networks. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

authors

• Huang, Tingwen

published proceedings

• NEUROCOMPUTING

author list (cited authors)

• Zhou, Y., Li, C., Chen, L., & Huang, T.

citation count

• 22

complete list of authors

• Zhou, Yinghua||Li, Chuandong||Chen, Ling||Huang, Tingwen

publication date

• January 2018

publisher

• Elsevier  Publisher

published in

• Neurocomputing  Journal

keywords

• Exponential Stability
• Impulse Time Window
• Memristive Cohen-grossberg Neural Networks
• Mixed Delays
• Stability Theory

Digital Object Identifier (DOI)

• 10.1016/j.neucom.2017.11.011

start page

• 2384

end page

• 2391

volume

• 275

URL

• http://dx.doi.org/10.1016/j.neucom.2017.11.011