Topics in matrix analysis
Global Robust Exponential Stability of Interval Neural Networks with Delays
Neural Processing Letters
Dynamical Behaviors of a Large Class of General Delayed Neural Networks
Neural Computation
Expert Systems with Applications: An International Journal
A delayed projection neural network for solving linear variational inequalities
IEEE Transactions on Neural Networks
New results for robust stability of dynamical neural networks with discrete time delays
Expert Systems with Applications: An International Journal
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A New Sufficient Condition for Global Robust Stability of Delayed Neural Networks
Neural Processing Letters
Robust stability of interval bidirectional associative memory neural network with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
This paper is concerned with the global asymptotic stability problem of dynamical neural networks with multiple time delays under parameter uncertainties. First carrying out an analysis of existence and uniqueness of the equilibrium point by means of the Homeomorphism theory, and then, studying the global asymptotic stability of the equilibrium point by constructing a suitable Lyapunov functional, we derive a new global robust stability criterion for the class of delayed neural networks with respect to the Lipschitz activation functions. The result obtained establishes a relationship between the neural network parameters only and it is independent of the time delay parameters. It is shown that the established stability condition generalizes some existing ones and it can be considered to an alternative result to some other corresponding results derived in previous literature. We also give some comparative numerical examples to demonstrate the validity and effectiveness of our proposed result.