2009Journal of Huaiyin Teachers CollegeRequires access

StabilityAnalysis of a Class of Hopfield Neural Networks under Thresholds

Jianzhong Liu

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Abstract

In this paper,we consider global stability of a neuron Hopfield neural networks with distributed delays under thresholds.For a neuron Hopfield neural networks,by using honeomorphic maps,we obtain some sufficient conditions which ensuring the existence and uniqueness of the equilibrium point;by using the technique of inequality and the method of the altering of constant,we obtain some sufficient conditions which ensuring the global asymptotic stability of the equilibrium point;by using Lyapunov functions,we obtain some sufficient conditions which ensuring the global exponential stability of the equilibrium point.

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What this paper is about

In this paper,we consider global stability of a neuron Hopfield neural networks with distributed delays under thresholds.For a neuron Hopfield neural networks,by using honeomorphic maps,we obtain some sufficient conditions which ensuring the existence and uniqueness of the equilibrium point;by using the technique of inequality and the method of the altering of constant,we obtain some sufficient conditions which ensuring the global asymptotic stability of the equilibrium point;by using Lyapunov functions,we obtain some sufficient conditions which ensuring the global exponential stability of the equilibrium point.

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Available abstract

In this paper,we consider global stability of a neuron Hopfield neural networks with distributed delays under thresholds.For a neuron Hopfield neural networks,by using honeomorphic maps,we obtain some sufficient conditions which ensuring the existence and uniqueness of the equilibrium point;by using the technique of inequality and the method of the altering of constant,we obtain some sufficient conditions which ensuring the global asymptotic stability of the equilibrium point;by using Lyapunov functions,we obtain some sufficient conditions which ensuring the global exponential stability of the equilibrium point.

Key concepts: Equilibrium point, Uniqueness, Exponential stability, Hopfield network, Artificial neural network, Constant (computer programming), Lyapunov function, Class (philosophy)

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