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Exponential Stability of Neutral-Type Impulsive Markovian Jump Neural Networks with General Incomplete Transition Rates

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Abstract

This paper is devoted to the investigation of exponential stability of Neutral-type impulsive Markovian jump neural networks with mixed time-varying delays and generally uncertain transition rates (GUTRs). Each transition rate can be completely unknown or only its estimate value is known in this GUTR model. This new uncertain model is more general than the existing ones. By utilizing Lyapunov–Krasovkii functional approach and linear matrix inequality technology, some novel globally exponentially stable results are derived. An example is given to show the effectiveness of the obtained results.

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Author information

Authors and Affiliations

  1. College of Information and Control Engineering, Weifang University, Weifang, 261061, People’s Republic of China

    Yunlong Liu & Chongsheng Hou

  2. School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai, 264209, People’s Republic of China

    Ce Zhang

  3. School of Science, Harbin Institute of Technology at Weihai, Weihai, 264209, People’s Republic of China

    Yonggui Kao

Authors
  1. Yunlong Liu
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  2. Ce Zhang
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  3. Yonggui Kao
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  4. Chongsheng Hou
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Corresponding authors

Correspondence to Yunlong Liu or Ce Zhang.

Additional information

This research is supported by the National Natural Science Foundations of China (61473097, 11501148), the State Key Program of Natural Science Foundation of China (U1533202), Shandong Province Natural Science Foundation (ZR2015AQ002), Shandong Province Higher Educational Science and Technology Program (J16LB10), Shandong Independent Innovation and Achievements Transformation Fund (2014CGZH1101), and funded by Civil Aviation Administration of China (MHRD20150104).

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Liu, Y., Zhang, C., Kao, Y. et al. Exponential Stability of Neutral-Type Impulsive Markovian Jump Neural Networks with General Incomplete Transition Rates. Neural Process Lett 47, 325–345 (2018). https://doi.org/10.1007/s11063-017-9650-2

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