This paper investigates the issue of finite time stability analysis of time-delayed neural networks by introducing a new Lyapunov functional which uses the information on the delay sufficiently and an augmented Lyapunov functional which contains some triple integral terms. Some improved delay-dependent stability criteria are derived using Jensen's inequality, reciprocally convex combination methods. Then, the finite-time stability conditions are solved by the linear matrix inequalities (LMIs). Numerical examples are finally presented to verify the effectiveness of the obtained results.
Improved Results on Finite-Time Stability Analysis of Neural Networks With Time-Varying Delays
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 30, 2017; final manuscript received March 2, 2018; published online May 2, 2018. Assoc. Editor: Dumitru I. Caruntu.
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Saravanan, S., and Syed Ali, M. (May 2, 2018). "Improved Results on Finite-Time Stability Analysis of Neural Networks With Time-Varying Delays." ASME. J. Dyn. Sys., Meas., Control. October 2018; 140(10): 101003. https://doi.org/10.1115/1.4039667
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