Stability of Impulsive Cohen-Grossberg Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms | Zendy

Jinhua Huang | Zendy; Jiqing Liu | Zendy; Guopeng Zhou | Zendy

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Stability of Impulsive Cohen-Grossberg Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Author(s) -

Jinhua Huang,

Jiqing Liu,

Guopeng Zhou

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/409758

Subject(s) - mathematics , stability (learning theory) , exponential stability , eigenvalues and eigenvectors , dirichlet distribution , reaction–diffusion system , diffusion , artificial neural network , work (physics) , laplace operator , dirichlet boundary condition , exponential function , boundary (topology) , mathematical analysis , boundary value problem , computer science , nonlinear system , mechanical engineering , engineering , physics , quantum mechanics , machine learning , thermodynamics

This work concerns the stability of impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms as well as Dirichlet boundary condition. By means of Poincaré inequality and Gronwall-Bellman-type impulsive integral inequality, we summarize some new and concise sufficient conditions ensuring the global exponential stability of equilibrium point. The proposed criteria are relevant to the diffusion coefficients and the smallest positive eigenvalue of corresponding Dirichlet Laplacian. In conclusion, two examples are illustrated to demonstrate the effectiveness of our obtained results

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