Title :
New Results for Global Robust Stability of Neural Networks with Time Delays
Author :
Yucel, Eylem ; Arik, Sabri
Author_Institution :
Istanbul Univ., Istanbul
Abstract :
Global robust convergence properties of continuous-time neural networks with discrete delays are studied. By employing suitable Lyapunov functionals, we derive a set of delay independent sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point. The conditions can be easily verified as they can be expressed in terms of the network parameters only. Some numerical examples are given to compare our results with previous robust stability results derived in the literature.
Keywords :
Lyapunov methods; continuous time systems; convergence; delay systems; discrete systems; neurocontrollers; robust control; Lyapunov functionals; continuous-time neural networks; discrete delays; global robust asymptotic stability; global robust convergence properties; time delays; Associative memory; Asymptotic stability; Computer networks; Convergence; Delay effects; Design optimization; Neural networks; Robust stability; Signal design; Signal processing;
Conference_Titel :
Intelligent Control, 2007. ISIC 2007. IEEE 22nd International Symposium on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0440-7
Electronic_ISBN :
2158-9860
DOI :
10.1109/ISIC.2007.4450922
