@Article{2018.100000021,
AUTHOR = {Guobao Zhang, Jing-Jing Xiong, Yongming Huang, Yong Lu, Ling Wang},
TITLE = {Delay-dependent Stability of Recurrent Neural Networks with  Time-varying Delay},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {3},
PAGES = {541--551},
URL = {http://www.techscience.com/iasc/v24n3/39779},
ISSN = {2326-005X},
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 stability criterion has 
a lower computational burden than some recent existing ones. Finally, two
numerical examples are considered to demonstrate the effectiveness of the 
presented stability results.},
DOI = {10.31209/2018.100000021}
}