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Discrete-time Cohen-Grossberg neural networks with transmission delays and impulses

In: Tatra Mountains Mathematical Publications, vol. 43, no. 2
Sannay Mohamad - Haydar Akça - Valéry Covachev

Details:

Year, pages: 2009, 145 - 161
Keywords:
Cohen-Grossberg neural networks, delays, discrete-time analogues, Lyapunov exponents.
About article:
A discrete-time analogue is formulated for an impulsive Cohen-Grossberg neural network with transmission delay in a manner in which the global exponential stability characteristics of a unique equilibrium point of the network are preserved. The formulation is based on extending the existing semi-discretization method that has been implemented for computer simulations of neural networks with linear stabilizing feedback terms. The exponential convergence in the $p$-norm of the analogue towards the unique equilibrium point is analyzed by exploiting an appropriate Lyapunov sequence and properties of an $M$-matrix. The main result yields a Lyapunov exponent that involves the magnitude and frequency of the impulses. One can use the result for deriving the exponential stability of non-impulsive discrete-time neural networks, and also for simulating the exponential stability of impulsive and non-impulsive continuous-time networks.
How to cite:
ISO 690:
Mohamad, S., Akça, H., Covachev, V. 2009. Discrete-time Cohen-Grossberg neural networks with transmission delays and impulses. In Tatra Mountains Mathematical Publications, vol. 43, no.2, pp. 145-161. 1210-3195.

APA:
Mohamad, S., Akça, H., Covachev, V. (2009). Discrete-time Cohen-Grossberg neural networks with transmission delays and impulses. Tatra Mountains Mathematical Publications, 43(2), 145-161. 1210-3195.