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Global Robust Exponential Stability for Interval Delayed Neural Networks with Possibly Unbounded Activation Functions

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Abstract

In this paper, we mainly study the global robust exponential stability of the neural networks with possibly unbounded activation functions. Based on the topological degree theory and Lyapunov functional method, we provide some new sufficient conditions for the global robust exponential stability. Under these conditions, we prove existence, uniqueness and global robust exponential stability of equilibrium point. In the end, some examples are provided to demonstrate the validity of the theoretical results.

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Acknowledgments

The authors would like to thank editor-in-chief, associate editor and eight anonymous reviewers for their insightful and constructive comments, which helped to enrich the content and improve the presentation of this paper. This work was supported by the national science fund of grant (10971043, 11126218, 11101107), Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT.NSRIF.201015,HIT.NSRIF.201017,HIT.NSRIF.2009157), Shandong Provincial Natural Science Foundation of China (ZR2011AM004).

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Authors and Affiliations

  1. Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai, 264209, People’s Republic of China

    Sitian Qin, Dejun Fan & Ming Yan

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

    Qinghe Liu

Authors
  1. Sitian Qin
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  2. Dejun Fan
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  3. Ming Yan
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  4. Qinghe Liu
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Correspondence to Dejun Fan.

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Qin, S., Fan, D., Yan, M. et al. Global Robust Exponential Stability for Interval Delayed Neural Networks with Possibly Unbounded Activation Functions. Neural Process Lett 40, 35–50 (2014). https://doi.org/10.1007/s11063-013-9309-6

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  • DOI: https://doi.org/10.1007/s11063-013-9309-6

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