Applied Mathematics and Mechanics

, Volume 23, Issue 12, pp 1367–1373

On the asymptotic behavior of hopfield neural network with periodic inputs

  • Xiang Lan
  • Zhou Jin
  • Liu Zeng-rong
  • Sun Shu
Article

DOI: 10.1007/BF02438376

Cite this article as:
Lan, X., Jin, Z., Zeng-rong, L. et al. Appl Math Mech (2002) 23: 1367. doi:10.1007/BF02438376

Abstract

Without assuming the boundedness and differentiability of the nonlinear activation functions, the new sufficient conditions of the existence and the global exponential stability of periodic solutions for Hopfield neural network with periodic inputs are given by using Mawhin's coincidence degree theory and Liapunov's function method.

Key words

Hopfield neural networkperiodic solutionglobal exponential stabilityconcidence degreeLiapunov's function

CLC numbers

O175TN911

Copyright information

© Editorial Committee of Applied Mathematics and Mechanics All rights reserved 1980

Authors and Affiliations

  • Xiang Lan
    • 1
  • Zhou Jin
    • 1
    • 2
  • Liu Zeng-rong
    • 2
  • Sun Shu
    • 3
  1. 1.Department of PhysicsHebei University of TechnologyTianjinP R China
  2. 2.Department of MathematicsShanghai UniversityShanghaiP R China
  3. 3.Naval Submarine AcademyQingdaoP R China