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Mathematics 2019, 7(2), 138; https://doi.org/10.3390/math7020138

Global Asymptotical Stability Analysis for Fractional Neural Networks with Time-Varying Delays

1
School of Mathematics Sciences, Anhui University, Hefei 230601, China
2
School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China
3
School of Mathematics, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Received: 1 December 2018 / Revised: 24 January 2019 / Accepted: 30 January 2019 / Published: 1 February 2019
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Abstract

In this paper, the global asymptotical stability of Riemann-Liouville fractional-order neural networks with time-varying delays is studied. By combining the Lyapunov functional function and LMI approach, some sufficient criteria that guarantee the global asymptotical stability of such fractional-order neural networks with both discrete time-varying delay and distributed time-varying delay are derived. The stability criteria is suitable for application and easy to be verified by software. Lastly, some numerical examples are presented to check the validity of the obtained results. View Full-Text
Keywords: time-varying delay; global asymptotical stability; fractional-order; neural networks; linear matrix inequality time-varying delay; global asymptotical stability; fractional-order; neural networks; linear matrix inequality
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, Z.; Zhang, Y.; Liu, J.-B.; Wei, J. Global Asymptotical Stability Analysis for Fractional Neural Networks with Time-Varying Delays. Mathematics 2019, 7, 138.

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