An analysis of global robust stability of delayed dynamical neural networks

Yucel E.

NEUROCOMPUTING, vol.165, pp.436-443, 2015 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 165
  • Publication Date: 2015
  • Doi Number: 10.1016/j.neucom.2015.03.070
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.436-443
  • Istanbul University Affiliated: Yes

Abstract

This paper studies the problem of establishing robust asymptotic stability of neural networks with multiple time delays and in the presence of the parameter uncertainties of the network. A new sufficient condition ensuring robust asymptotic stability is presented by manipulating the properties of some certain classes of real matrices and employing Homomorphic mapping and Lyapunov stability theorems. A numerical example is given to show that the condition obtained can outperform alternative ones in terms of conservatism and computational complexity. (C) 2015 Elsevier B.V. All rights reserved.