Premium
Global exponential stability for a class of generalized delayed neural networks with impulses
Author(s) -
Xi Qiang
Publication year - 2011
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.1451
Subject(s) - mathematics , exponential stability , class (philosophy) , equilibrium point , artificial neural network , stability (learning theory) , lyapunov function , matrix (chemical analysis) , exponential function , control theory (sociology) , mathematical analysis , nonlinear system , computer science , control (management) , differential equation , artificial intelligence , physics , materials science , quantum mechanics , machine learning , composite material
In this paper, by utilizing Lyapunov functional method, the quality of negative definite matrix and the linear matrix inequality approach, the global exponential stability of the equilibrium point for a class of generalized delayed neural networks with impulses is investigated. A new criterion on global exponential stability is obtained. The result is related to the size of delays and impulses. An example is given to illustrate the effectiveness of our result. Copyright © 2011 John Wiley & Sons, Ltd.