Title :
Global robust exponential stability for Cohen-Grossberg neural networks with time-varying delays
Author :
Xiaolin Li ; Jia Jia
Author_Institution :
Dept. of Math., Univ. of Shanghai, Shanghai, China
Abstract :
Global robust exponential stability problems for Cohen-Grossberg neural networks are investigated in this paper. New sufficient conditions are derived to ensure the global robust exponential stability of the equilibrium point by using a new inequality and linear matrix inequality technique. A numerical example is given to show the effectiveness of the theoretical results.
Keywords :
asymptotic stability; delays; linear matrix inequalities; neural nets; time-varying networks; Cohen-Grossberg neural network; equilibrium point; global robust exponential stability; linear matrix inequality technique; sufficient conditions; time-varying delay; Biological neural networks; Control theory; Delay; Delay effects; Robustness; Stability;
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
DOI :
10.1109/ICACI.2012.6463285
