Further Stability Analysis for Neural Networks with Time-Varying Interval Delay
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Adaptive Exponential Synchronization of Stochastic Delay Neural Networks with Reaction-Diffusion
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust stability of Cohen-Grossberg neural networks via state transmission matrix
IEEE Transactions on Neural Networks
Almost sure exponential stability of recurrent neural networks with Markovian switching
IEEE Transactions on Neural Networks
Global Exponential Stability of Recurrent Neural Networks with Time-Dependent Switching Dynamics
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay
Information Sciences: an International Journal
Exponential Stability of Uncertain Stochastic Neural Networks with Markovian Switching
Neural Processing Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust stability of delayed fuzzy Cohen-Grossberg neural networks
Computers & Mathematics with Applications
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
By combining Cohen-Grossberg neural networks with an arbitrary switching rule, the mathematical model of a class of switched Cohen-Grossberg neural networks with mixed time-varying delays is established. Moreover, robust stability for such switched Cohen-Grossberg neural networks is analyzed based on a Lyapunov approach and linear matrix inequality (LMI) technique. Simple sufficient conditions are given to guarantee the switched Cohen-Grossberg neural networks to be globally asymptotically stable for all admissible parametric uncertainties. The proposed LMI-based results are computationally efficient as they can be solved numerically using standard commercial software. An example is given to illustrate the usefulness of the results