Letters: Stability in static delayed neural networks: A nonlinear measure approach

Authors:
Ping Li;Jinde Cao
Affiliations:
Department of Mathematics, Southeast University, Nanjing 210096, China;Department of Mathematics, Southeast University, Nanjing 210096, China
Venue:
Neurocomputing
Year:
2006

Citing 7
Cited 9

Original Contribution: Associative memory with nonmonotone dynamics

Neural Networks
Iterative solution of nonlinear equations in several variables

Iterative solution of nonlinear equations in several variables
On the stability of globally projected dynamical systems

Journal of Optimization Theory and Applications
A comparative study of two modeling approaches in neural networks

Neural Networks
A reference model approach to stability analysis of neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear measures: a new approach to exponential stability analysis for Hopfield-type neural networks

IEEE Transactions on Neural Networks
A generalized LMI-based approach to the global asymptotic stability of delayed cellular neural networks

IEEE Transactions on Neural Networks

Letters: Comments and further improvements on "New LMI conditions for delay-dependent asymptotic stability of delayed Hopfield neural networks"

Neurocomputing
Global exponential periodicity of three-unit neural networks in a ring with time-varying delays

Neurocomputing
Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions

Neural Processing Letters
The Dahlquist Constant Approach to Stability Analysis of the Static Neural Networks

ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Passivity Analysis of Neural Networks with Time-Varying Delays of Neutral Type

ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach

IEEE Transactions on Circuits and Systems II: Express Briefs
Global exponential stability of Cohen-Grossberg neural network with time varying delays

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
State estimation for static neural networks with time-varying delay

Neural Networks
Novel stability criteria of Cohen–Grossberg neural networks with time-varying delays

International Journal of Circuit Theory and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, the global exponential stability is discussed for static recurrent neural networks. Without assuming the boundedness, monotonicity and differentiability of the activation functions, a new sufficient condition is obtained to ensure the existence and uniqueness of the equilibrium based on the nonlinear measure. Meanwhile, the condition obtained also guarantees the global exponential stability of the delayed neural networks via constructing a proper Lyapunov functional. The results, which are independent of the time delay, can be checked easily by convex optimization algorithms. In the end of this paper, two illustrative examples are also given to show the effectiveness of our results.