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    Delay-dependent Stability of Recurrent Neural Networks with Time-varying Delay

    Guobao Zhanga,b, Jing-Jing Xionga,b, Yongming Huanga,b, Yong Lua,b,c, Ling Wanga,b

    Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 541-551, 2018, DOI:10.31209/2018.100000021

    Abstract This paper investigates the delay-dependent stability problem of recurrent neural networks with time-varying delay. A new and less conservative stability criterion is derived through constructing a new augmented Lyapunov-Krasovskii functional (LKF) and employing the linear matrix inequality method. A new augmented LKF that considers more information of the slope of neuron activation functions is developed for further reducing the conservatism of stability results. To deal with the derivative of the LKF, several commonly used techniques, including the integral inequality, reciprocally convex combination, and free-weighting matrix method, are applied. Moreover, it is found that the obtained More >

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