Frustration, stability, and delay-induced oscillations in a neural network model
SIAM Journal on Applied Mathematics
Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Global exponential stability of delayed Hopfield neural networks
Neural Networks
Stability analyses of cellular neural networks with continuous time delay
Journal of Computational and Applied Mathematics
Globally exponential stability conditions for cellular neural networks with time-varying delays
Applied Mathematics and Computation
Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Exponential Convergence of Delayed Dynamical Systems
Neural Computation
Stability in cellular neural networks with a piecewise constant argument
Journal of Computational and Applied Mathematics
Delay-dependent exponential stability for a class of neural networks with time delays
Journal of Computational and Applied Mathematics
Method of Lyapunov functions for differential equations with piecewise constant delay
Journal of Computational and Applied Mathematics
An analysis of global asymptotic stability of delayed cellular neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Delayed Cellular Neural Networks
IEEE Transactions on Neural Networks
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In this paper, we apply the method of Lyapunov functions for differential equations with piecewise constant argument of generalized type to a model of recurrent neural networks (RNNs). The model involves both advanced and delayed arguments. Sufficient conditions are obtained for global exponential stability of the equilibrium point. Examples with numerical simulations are presented to illustrate the results.