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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