In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for recurrent neural networks (RNNs) with parametric uncertainties and time-varying delay are studied. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. The developed stability conditions are in terms of linear matrix inequalities (LMIs) and the integral inequality approach (IIA), which can be checked easily by recently developed algorithms solving LMIs. Furthermore, the proposed stability conditions are less conservative than some recently known ones in the literature, and this has been demonstrated via four examples with simulation.
Further Results on Exponential Robust Stability Analysis for Recurrent Neural Networks With Time-Varying Delay
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 26, 2013; final manuscript received November 6, 2014; published online January 9, 2015. Assoc. Editor: YangQuan Chen.
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Liu, P. (April 1, 2015). "Further Results on Exponential Robust Stability Analysis for Recurrent Neural Networks With Time-Varying Delay." ASME. J. Dyn. Sys., Meas., Control. April 2015; 137(4): 041018. https://doi.org/10.1115/1.4029060
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