Abstract
This paper analyzes the robustness of globally exponential stability of time-varying delayed neural networks (NNs) subjected to random disturbances. Given a globally exponentially stable neural network, and in the presence of noise, we quantify how much noise intensity that the delayed neural network can remain to be globally exponentially stable. We characterize the upper bounds of the noise intensity for the delayed NNs to sustain globally exponential stability. The upper bounds of parameter uncertainty intensity are characterized by using transcendental equation. A numerical example is provided to illustrate the theoretical result.
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Acknowledgments
The authors would like to thank the associate editor and the referees for their detailed comments and valuable suggestions, which considerably improved the presentation of this paper. This work was supported by the Key Program of National Natural Science Foundation of China with Grant No. 61134012, National Natural Science Foundation of China with Grant No. 61203055, 11271146 and supported by the Fundamental Research Funds for the Central Universities of 2013XK03.
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Zhu, S., Luo, W., Li, J. et al. Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances. Neural Comput & Applic 25, 743–749 (2014). https://doi.org/10.1007/s00521-014-1547-7
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DOI: https://doi.org/10.1007/s00521-014-1547-7