Discrete-time versus continuous-time models of neural networks
Journal of Computer and System Sciences
On delayed impulsive Hopfield neural networks
Neural Networks
On impulsive autoassociative neural networks
Neural Networks
Dynamics of a class of discete-time neural networks and their comtinuous-time counterparts
Mathematics and Computers in Simulation
Discrete-time analogues of integrodifferential equations modelling bidirectional neural networks
Journal of Computational and Applied Mathematics
Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays
Neural Processing Letters
Exponential stability of impulsive high-order Hopfield-type neural networks with time-varying delays
IEEE Transactions on Neural Networks
Journal of Computational and Applied Mathematics
A unified treatment for stability preservation in computer simulations of impulsive BAM networks
Computers & Mathematics with Applications
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This paper demonstrates the use of a semi-discretization technique for obtaining a discrete-time analogue of an exponentially convergent network that is subject to impulses with large magnitude. Prior to implementing the analogue for computer simulations, we investigate its exponential convergence towards a unique equilibrium state and thereby obtain a family of sufficiency conditions governing the network parameters and the impulse magnitude and frequency. Although the time-step does not appear in the conditions that govern the network parameters, its value needs to be sufficiently small in order for the analogue displays correct convergence behaviour of the network when subjected particularly to large impulses.