Fixed-time stabilization of impulsive Cohen-Grossberg BAM neural networks. Academic Article

abstract

• This article is concerned with the fixed-time stabilization for impulsive Cohen-Grossberg BAM neural networks via two different controllers. By using a novel constructive approach based on some comparison techniques for differential inequalities, an improvement theorem of fixed-time stability for impulsive dynamical systems is established. In addition, based on the fixed-time stability theorem of impulsive dynamical systems, two different control protocols are designed to ensure the fixed-time stabilization of impulsive Cohen-Grossberg BAM neural networks, which include and extend the earlier works. Finally, two simulations examples are provided to illustrate the validity of the proposed theoretical results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Li, H., Li, C., Huang, T., & Zhang, W.

citation count

• 80

complete list of authors

• Li, Hongfei||Li, Chuandong||Huang, Tingwen||Zhang, Wanli

publication date

• February 2018

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Bam Neural Networks
• Cohen–grossberg Neural Networks
• Fixed-time Stability
• Impulses
• Neural Networks, Computer
• Stabilization
• Time Factors

PubMed Central ID

• 29268197

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2017.11.017

start page

• 203

end page

• 211

volume

• 98

URL

• http://dx.doi.org/10.1016/j.neunet.2017.11.017