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Robust stability analysis for uncertain recurrent neural networks with leakage delay based on delay-partitioning approach

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Abstract

This paper focuses on the issue of robust stability analysis for recurrent neural networks (RNNs) with leakage delay. By constructing a novel Lyapunov–Krasovskii functional together with the reciprocally convex approach and the free-weighting matrix technique, some less conservative stability criteria in terms of linear matrix inequalities for RNNs are derived. The new contribution of this paper is that a novel delay-partitioning method is proposed, and some new zero equalities are introduced. Finally, several examples are given to demonstrate the effectiveness of the proposed methods. The simulated results reveal that the leakage delay has great influence on the dynamical systems, and it cannot be neglected.

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Acknowledgements

The authors are very thankful to the editor and reviewers for their useful comments and helpful suggestions. This work is partly supported by NSFC under Grant Nos. 61271355 and 61375063, the ZNDXYJSJGXM under Grant No. 2015J-GB21 and the Educational Department of Hunan Province of China under Grant No. 15C0243.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Changsha, 410083, Hunan, China

    Sai-Bing Qiu, Xin-Ge Liu, Feng-Xian Wang & Yan-Jun Shu

  2. College of Mathematics and Computer Science, Hunan City University, Yiyang, 413000, Hunan, China

    Sai-Bing Qiu

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  1. Sai-Bing Qiu
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  2. Xin-Ge Liu
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  3. Feng-Xian Wang
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  4. Yan-Jun Shu
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Correspondence to Xin-Ge Liu.

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Qiu, SB., Liu, XG., Wang, FX. et al. Robust stability analysis for uncertain recurrent neural networks with leakage delay based on delay-partitioning approach. Neural Comput & Applic 30, 211–222 (2018). https://doi.org/10.1007/s00521-016-2670-4

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  • DOI: https://doi.org/10.1007/s00521-016-2670-4

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