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Finite-time stability criteria for a class of fractional-order neural networks with delay

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Abstract

Finite-time stabilities of a class of fractional-order neural networks delayed systems with order \(\alpha {:}\) \(0<\alpha \le 0.5\) and \(0.5<\alpha <1\) are addressed in this paper, respectively. By using inequality technique, two new delay-dependent sufficient conditions ensuring stability of such fractional-order neural networks over a finite-time interval are obtained. Obtained conditions are less conservative than that given in the earlier references. Two numerical examples are given to show the effectiveness of our proposed method.

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Acknowledgments

This work was supported by the National Natural Science Funds of China for Distinguished Young Scholar under Grant (No. 50925727), the National Natural Science Foundation of China (Nos. 61403115, 61374135), the National Defense Advanced Research Project Grant (Nos. C1120110004, 9140 A27020211DZ5102) and the Key Grant Project of Chinese Ministry of Education under Grant (No. 313018).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Liping Chen, Cong Liu & Yigang He

  2. School of Mathematics, Anhui University, Hefei, 230039, China

    Ranchao Wu

  3. School of Automation, Chongqing University, Chongqing, 400044, China

    Yi Chai

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  1. Liping Chen
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  2. Cong Liu
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  3. Ranchao Wu
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  4. Yigang He
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Correspondence to Liping Chen.

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Chen, L., Liu, C., Wu, R. et al. Finite-time stability criteria for a class of fractional-order neural networks with delay. Neural Comput & Applic 27, 549–556 (2016). https://doi.org/10.1007/s00521-015-1876-1

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