The problem of passivity analysis of uncertain neural networks (UNNs) with discrete and distributed delay is considered. By constructing a suitable augmented Lyapunov-Krasovskii functional(LKF) and combing a novel integral inequality with convex approach to estimate the derivative of the proposed LKF, improved sufficient conditions to guarantee passivity of the concerned neural networks are established with the framework of linear matrix inequalities(LMIs), which can be solved easily by various efficient convex optimization algorithms. Two numerical examples are provided to demonstrate the enhancement of feasible region of the proposed criteria by the comparison of maximum allowable delay bounds.

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passivity of passivity analysis of uncertain neural networks augmented lyapunovkrasovskii functionallkf convex optimization algorithms discrete and distributed delay feasible region linear matrix inequalitieslmis maximum allowable delay bounds derivative integral inequality with convex approach

Bin Yang, Juan Wang, Hongbing Zeng, Mengnan Hao,.Further results on passivity analysis for uncertain neural networks with discrete and distributed delays. 430–431 (0),.

Bin Yang, et al."Further results on passivity analysis for uncertain neural networks with discrete and distributed delays" 430–431

Bin Yang, Juan Wang, Hongbing Zeng, Mengnan Hao,"Further results on passivity analysis for uncertain neural networks with discrete and distributed delays"