Global robust exponential stability analysis for delayed recurrent neural networks

Author

Zhang, Zhizhou ; Zhang, Lingling ; She, Longhua ; Huang, Lihong

Author_Institution

Dept. of Mechatron. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha

fYear

2008

fDate

20-23 June 2008

Firstpage

499

Lastpage

503

Abstract

This paper provides a new sufficient condition for the global robust exponential stability of a delayed recurrent neural network. The conditions are expressed in terms of LMIs, which can be easily checked by various recently developed algorithms in solving convex optimization problems. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition.

Keywords

asymptotic stability; convex programming; delay systems; linear matrix inequalities; neurocontrollers; recurrent neural nets; robust control; LMI; convex optimization problem; delayed recurrent neural network; global robust exponential stability analysis; linear matrix inequality; Artificial neural networks; Automation; Mathematics; Mechatronics; Neural networks; Neurons; Recurrent neural networks; Robust stability; Stability analysis; Symmetric matrices; Delayed recurrent neural networks; Global exponential stability; Interval systems; Linear matrix inequality;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation, 2008. ICIA 2008. International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-2183-1

Electronic_ISBN

978-1-4244-2184-8

Type

conf

DOI

10.1109/ICINFA.2008.4608051

Filename

4608051