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New results for global stability of a class of neutral-type neural systems with time delays. (English) Zbl 1170.34352
Summary: This paper studies the global convergence properties of a class of neutral-type neural networks with discrete time delays. This class of neutral systems includes Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks. Based on the Lyapunov stability theorems, some delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for this class of neutral-type systems are derived. It is shown that the results presented in this paper for neutral-type delayed neural networks are the generalization of a recently reported stability result. A numerical example is also given to demonstrate the applicability of our proposed stability criteria.
MSC:
34K20Stability theory of functional-differential equations
34K25Asymptotic theory of functional-differential equations
92B20General theory of neural networks (mathematical biology)
34K40Neutral functional-differential equations
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