Robust stability of interval neural networks with mixed time-delays via augmented functional
基于增广泛函的混合时滞区间神经网络的鲁棒稳定性

Global robust stability of interval recurrent neural networks with mixed time-varying delays (discrete timevarying delay and distributed time-varying delay) is investigated. Being different from existing reports, the novel delaydependent robust stability criteria for interval recurrent neural networks with mixed time-varying delays employ a new augmented Lyapunov-Krasovskii functional. In the new augmented functional, we introduce an integral term to the activation function, which gives a preferable representation of the relation between states of the system and the activation function. Because of the new functional, the criteria proposed in this paper are less conservative than the currently existing ones. Moreover, the employment of the Jensen inequality in proving the criteria relaxes the restriction on the time derivative of the time-varying delay in the proposed criteria. The simulation is provided to verify the effectiveness of the proposed results.