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**Global robust stability criteria of stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays.**
*(English)*
Zbl 1221.37196

Summary: The paper is concerned with the problem of robust asymptotic stability analysis of stochastic Cohen-Grossberg neural networks with discrete and distributed time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technology, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed conditions can be checked easily with the LMI Control Toolbox in Matlab. Furthermore, all the results are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. A numerical example is given to demonstrate the effectiveness of our results.

### MSC:

37N25 | Dynamical systems in biology |

34K20 | Stability theory of functional-differential equations |

34F05 | Ordinary differential equations and systems with randomness |

60H10 | Stochastic ordinary differential equations (aspects of stochastic analysis) |

62M45 | Neural nets and related approaches to inference from stochastic processes |

92B20 | Neural networks for/in biological studies, artificial life and related topics |

### Keywords:

stochastic Cohen-Grossberg neural networks; delay-dependent robust stability; discrete and distributed time-varying delays; norm-bounded uncertainties### Software:

Matlab
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\textit{W. Su} and \textit{Y. Chen}, Commun. Nonlinear Sci. Numer. Simul. 14, No. 2, 520--528 (2009; Zbl 1221.37196)

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### References:

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