Title :
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
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;
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
DOI :
10.1109/ICINFA.2008.4608051
