2006Unpublished venueRequires access

Stability Analysis of Continuous Hopfield Neural Networks with Delay

Jin Cong, Shihui Wang

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Abstract

In this paper, by constructing a new Lyapunov functional, problem of the global asymptotic stability is discussed for the continuous Hopfield neural networks with delays. A simple and new sufficient condition is obtained ensuring existence, uniqueness of the equilibrium point and its global asymptotic stability of the neural networks. This condition can be used to design globally asymptotic stable networks and thus have important significance in both theory and applications.

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What this paper is about

In this paper, by constructing a new Lyapunov functional, problem of the global asymptotic stability is discussed for the continuous Hopfield neural networks with delays. A simple and new sufficient condition is obtained ensuring existence, uniqueness of the equilibrium point and its global asymptotic stability of the neural networks. This condition can be used to design globally asymptotic stable networks and thus have important significance in both theory and applications.

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Available abstract

In this paper, by constructing a new Lyapunov functional, problem of the global asymptotic stability is discussed for the continuous Hopfield neural networks with delays. A simple and new sufficient condition is obtained ensuring existence, uniqueness of the equilibrium point and its global asymptotic stability of the neural networks. This condition can be used to design globally asymptotic stable networks and thus have important significance in both theory and applications.

Key concepts: Hopfield network, Exponential stability, Uniqueness, Equilibrium point, Artificial neural network, Simple (philosophy), Stability (learning theory), Lyapunov function

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