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Region Stability and Stabilization of Recurrent Neural Network with Parameter Disturbances

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Abstract

This paper mainly focuses on global region stability and stabilization analysis for recurrent neural networks with certain or uncertain parameter disturbances. Firstly, it presents global region stability results for recurrent neural networks with certain parameter disturbances by state partition and mathematical analysis methods. Next, it designs one adaptive controller to stabilize network states to the desired region for recurrent neural networks with uncertain parameter disturbances. At last, it gives two numerical examples for verifying obtained results.

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Acknowledgements

The authors would like to thank the associate editor and the reviewers for their detailed comments and valuable suggestions which considerably improved the presentation of the paper. The work is supported by the Natural Science Foundation of China under Grant 61876097.

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Authors and Affiliations

  1. Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, China Three Gorges University, Yichang, 443002, China

    Gang Bao, Yue Peng, Xue Zhou & Shunqi Gong

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  1. Gang Bao
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  2. Yue Peng
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  3. Xue Zhou
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  4. Shunqi Gong
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Correspondence to Gang Bao.

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Bao, G., Peng, Y., Zhou, X. et al. Region Stability and Stabilization of Recurrent Neural Network with Parameter Disturbances. Neural Process Lett 52, 2175–2188 (2020). https://doi.org/10.1007/s11063-020-10344-2

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