Premium
Global robust stability of interval delayed neural networks: Modified approach
Author(s) -
Singh Vimal
Publication year - 2009
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.523
Subject(s) - interval (graph theory) , artificial neural network , stability (learning theory) , hopfield network , stability criterion , mathematics , linear matrix inequality , control theory (sociology) , circle criterion , matrix (chemical analysis) , computer science , mathematical optimization , exponential stability , nonlinear system , artificial intelligence , statistics , discrete time and continuous time , combinatorics , control (management) , physics , materials science , machine learning , quantum mechanics , composite material
Abstract A criterion for the global robust stability of Hopfield‐type delayed neural networks with the intervalized network parameters is presented. The criterion, which is derived by utilizing the idea of splitting the given interval into two intervals, is in the form of linear matrix inequality and, hence, computationally tractable. The criterion yields a less conservative condition compared with many recently reported criteria, as is demonstrated with an example. Copyright © 2008 John Wiley & Sons, Ltd.