Globally Exponential Stability of Impulsive Neural Networks with Given Convergence Rate | Zendy

Chengyan Liu | Zendy; Xiaodi Li | Zendy; Xilin Fu | Zendy

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Globally Exponential Stability of Impulsive Neural Networks with Given Convergence Rate

Author(s) -

Chengyan Liu,

Xiaodi Li,

Xilin Fu

Publication year - 2013

Publication title -

advances in artificial neural systems

Language(s) - English

Resource type - Journals

eISSN - 1687-7608

pISSN - 1687-7594

DOI - 10.1155/2013/908602

Subject(s) - exponential stability , convergence (economics) , artificial neural network , rate of convergence , stability (learning theory) , lyapunov function , computer science , exponential function , mathematics , class (philosophy) , control theory (sociology) , mathematical optimization , function (biology) , artificial intelligence , machine learning , control (management) , economics , mathematical analysis , nonlinear system , computer network , channel (broadcasting) , physics , quantum mechanics , evolutionary biology , biology , economic growth

This paper deals with the stability problem for a class of impulsive neural networks. Some sufficient conditions which can guarantee the globally exponential stability of the addressed models with given convergence rate are derived by using Lyapunov function and impulsive analysis techniques. Finally, an example is given to show the effectiveness of the obtained results

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