Title :
A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks
Author_Institution :
Dept. of Electr.- Electron. Eng., Atilim Univ., Ankara, Turkey
Abstract :
A novel linear matrix inequality (LMI)-based criterion for the global asymptotic stability and uniqueness of the equilibrium point of a class of delayed cellular neural networks (CNNs) is presented. The criterion turns out to be a generalization and improvement over some previous criteria.
Keywords :
asymptotic stability; cellular neural nets; linear matrix inequalities; DCNNs; delayed cellular neural networks; equilibrium point; generalized LMI-based approach; global asymptotic stability; linear matrix inequality; Asymptotic stability; Cellular neural networks; Delay; Equations; Linear matrix inequalities; Object detection; Output feedback; Stability analysis; State feedback; Symmetric matrices; Linear Models; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2003.820616
