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Link to original content: https://doi.org/10.1007/s13042-016-0547-6
Global Lagrange stability for neutral type neural networks with mixed time-varying delays | International Journal of Machine Learning and Cybernetics Skip to main content
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Global Lagrange stability for neutral type neural networks with mixed time-varying delays

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Abstract

In this paper, the global exponential stability in Lagrange sense for neutral type neural networks with mixed time-varying delays is studied. By constructing proper Lyapunov functions and using inequality techniques, new delay-dependent succinct criteria are derived to ensure the global exponential Lagrange stability for the aforementioned neural networks. Meanwhile, globally exponentially attractive sets are given out. The results obtained here are more general than some of existing results. Finally, two examples are presented and analyzed to validate our results.

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China under Grant No. 61174216, the Scientific and Technological Research Program of Chongqing Municipal Education Commission under Grant Nos. KJ1501002 and KJ1401003, and the Youth Fund of Chongqing Three Gorges University under Grant No. 14QN22.

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Authors and Affiliations

  1. Key Laboratory for Nonlinear Science and System Structure, and School of Mathematics and Statistics, Chongqing Three Gorges University, Wanzhou, 404100, China

    Zhengwen Tu & Liangwei Wang

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  1. Zhengwen Tu
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  2. Liangwei Wang
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Correspondence to Liangwei Wang.

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Tu, Z., Wang, L. Global Lagrange stability for neutral type neural networks with mixed time-varying delays. Int. J. Mach. Learn. & Cyber. 9, 599–609 (2018). https://doi.org/10.1007/s13042-016-0547-6

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