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A new global robust stability condition for uncertain neural networks with discrete and distributed delays

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Abstract

This paper addresses the global robust stability for uncertain neural networks with discrete and distributed delays. By using the Lyapunov stability theory, homomorphic mapping theory and matrix theory, improved sufficient robust stability conditions for uncertain neural networks with mixed delays are presented. Our results can be verified easily. The advantage of the proposed results is that they can be expressed in terms of network parameters only. Finally, two illustrative numerical examples are provided to show the validity comparing with the existing corresponding results.

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Acknowledgements

This work was supported by the Natural Science Foundation of the Anhui Higher Education Institutions of China under Grant nos. KJ2016A625, KJ2016A555, the program for excellent young talents in university of Anhui province under Grant no. gxyq2017158.

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Authors and Affiliations

  1. School of Mathematical Sciences, Huaibei Normal University, Huaibei, 235000, China

    Hao Chen

  2. School of Information Engineering, Fuyang normal university, Fuyang, 236041, China

    Wei Kang

  3. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hao Chen, Wei Kang & Shouming Zhong

Authors
  1. Hao Chen
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  2. Wei Kang
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  3. Shouming Zhong
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Correspondence to Wei Kang.

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Chen, H., Kang, W. & Zhong, S. A new global robust stability condition for uncertain neural networks with discrete and distributed delays. Int. J. Mach. Learn. & Cyber. 10, 1025–1035 (2019). https://doi.org/10.1007/s13042-017-0779-0

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  • DOI: https://doi.org/10.1007/s13042-017-0779-0

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