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**Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: an LMI approach.**
*(English)*
Zbl 1217.34126

Summary: We consider stochastic Cohen-Grossberg-type BAM neural networks with mixed delays. By utilizing a Lyapunov-Krasovskii functional and the Linear Matrix Inequality (LMI) approach, some sufficient LMI-based conditions are obtained to guarantee the global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays. These conditions can be easily checked via the Matlab LMI toolbox. Moreover, the obtained results extend and improve earlier publications. Finally, a numerical example is provided to demonstrate the low conservatism and the effectiveness of the proposed LMI conditions.

### MSC:

34K50 | Stochastic functional-differential equations |

34K20 | Stability theory of functional-differential equations |

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

### Keywords:

Cohen-Grossberg-type BAM neural networks; linear matrix inequality; Lyapunov-Krasovskii functional; time-varying delays; distributed delays; stochastic effect
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\textit{X. Li} and \textit{X. Fu}, J. Comput. Appl. Math. 235, No. 12, 3385--3394 (2011; Zbl 1217.34126)

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