Necessary and sufficient condition for the absolute exponential stability of a class of neural networks with finite delay Academic Article

abstract

• In this Letter, a necessary and sufficient condition is established for ascertaining the absolute exponential stability for a class of finite delayed neural networks with connection matrices A having nonnegative off-diagonal elements, delayed feedback matrix A having nonnegative elements and strictly increasing and Lipschitz activation functions. 2005 Elsevier B.V. All rights reserved.

authors

• Huang, Tingwen

published proceedings

• PHYSICS LETTERS A

author list (cited authors)

• Huang, T. W., Cao, J. D., & Li, C. D.

citation count

• 19

complete list of authors

• Huang, TW||Cao, JD||Li, CD

publication date

• March 2006

publisher

• Elsevier  Publisher

published in

• Physics Letters, Section A: General, Atomic and Solid State Physics  Journal

keywords

• Absolute Exponential Stability
• Exponential Stability
• Finite Delay
• M-matrix
• Necessary And Sufficient Condition
• Neural Networks

Digital Object Identifier (DOI)

• 10.1016/j.physleta.2005.11.038

start page

• 94

end page

• 98

volume

• 352

issue

• 1-2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.physleta.2005.11.038