Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach. Academic Article

abstract

• This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. One sufficient global exponential stability criterion is obtained based on this key matrix-measure and no Lyapunov function is required. To make the stability performance better, another stability criterion in which more detailed information is involved has been acquired. The theoretical results in this note contain and extend some existing continuous-time and discrete-time works. A numerical example is given to show the validity of the results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Xiao, Q., & Huang, T.

citation count

• 9

complete list of authors

• Xiao, Qiang||Huang, Tingwen

publication date

• October 2020

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Inertial Neural Network
• Neural Networks, Computer
• Reproducibility Of Results
• STABILITY
• Time Delay
• Time Factors
• Time Scale
• Unified Matrix-measure

PubMed Central ID

• 32598283

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2020.06.020

start page

• 33

end page

• 38

volume

• 130

URL

• http://dx.doi.org/10.1016/j.neunet.2020.06.020