Global convergence rate of recurrently connected neural networks
Neural Computation
New conditions on global stability of Cohen-Grossberg neural networks
Neural Computation
Exponential Convergence of Delayed Dynamical Systems
Neural Computation
Journal of Computational and Applied Mathematics
Evolving novelty detectors for specific applications
Neurocomputing
Robust exponential stability analysis for unertain neural networks with time delay
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
The L2-gaining steady analysis of Hopfield neural network with time varying parameters
ICNC'09 Proceedings of the 5th international conference on Natural computation
Dynamic analysis of a general class of winner-take-all competitive neural networks
IEEE Transactions on Neural Networks
Robust stability for delayed neural networks with nonlinear perturbation
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Impulsive robust control of interval hopfield neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
In this paper, we use the matrix measure technique to study the stability of dynamical neural networks. Testable conditions for global exponential stability of nonlinear dynamical systems and dynamical neural networks are given. It shows how a few well-known results can be unified and generalized in a straightforward way. Local exponential stability of a class of dynamical neural networks is also studied; we point out that the local exponential stability of any equilibrium point of dynamical neural networks is equivalent to the stability of the linearized system around that equilibrium point. From this, some well-known and new sufficient conditions for local exponential stability of neural networks are obtained