Input-to-state stability of delayed reaction-diffusion neural networks with impulsive effects Academic Article

abstract

• 2018 Elsevier B.V. In this article, we study input-to-state stability (ISS) of impulsive reaction-diffusion neural networks (RDNNs) with variable delays. Firstly, a class of impulsive RDNNs with variable delays is formulated, in which impulsive functions are nonlinear. Secondly, by constructing an impulsive delay differential inequality with variable inputs, we derive several novel criteria on ISS for the desired impulsive neural networks with reaction-diffusion term. Moreover, they are unveiled not only that the ISS of the RDNNs does not change under small perturbations of impulses, but also the impulsive effects can successfully stabilize the system even when the corresponding non-impulsive system is unstable. Finally, two examples and their simulations are provided to illustrate the effectiveness of our results and show how impulses affect ISS of the RDNNs.

authors

• Huang, Tingwen

published proceedings

• NEUROCOMPUTING

author list (cited authors)

• Yang, Z., Zhou, W., & Huang, T.

citation count

• 16

complete list of authors

• Yang, Zhichun||Zhou, Weisong||Huang, Tingwen

publication date

• March 2019

publisher

• Elsevier  Publisher

published in

• Neurocomputing  Journal

keywords

• Delay
• Impulse
• Input-to-state Stability
• Reaction-diffusion Neural Networks

Digital Object Identifier (DOI)

• 10.1016/j.neucom.2018.12.019

start page

• 261

end page

• 272

volume

• 333

URL

• http://dx.doi.org/10.1016/j.neucom.2018.12.019