Finite-Time Stabilization of Competitive Neural Networks With Time-Varying Delays. Academic Article

abstract

• This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Cybern

altmetric score

• 0.25

author list (cited authors)

• Sheng, Y., Zeng, Z., & Huang, T.

citation count

• 6

complete list of authors

• Sheng, Yin||Zeng, Zhigang||Huang, Tingwen

publication date

• November 2022

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE TRANSACTIONS ON CYBERNETICS  Journal

keywords

• Feedback
• Neural Networks, Computer
• Time

Digital Object Identifier (DOI)

• 10.1109/TCYB.2021.3082153

start page

• 11325

end page

• 11334

volume

• 52

issue

• 11

URL

• http://dx.doi.org/10.1109/tcyb.2021.3082153