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Lagrange Stability for Delayed-Impulses in Discrete-Time Cohen–Grossberg Neural Networks with Delays

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Abstract

In this paper, the problem of exponential Lagrange stability for delayed-impulses in discrete-time Cohen–Grossberg neural networks (CGNNs) with delays is considered. By establishing a novel convergent difference inequation, combining with inductive method and Lyapunov theory, some sufficient conditions are obtained to ensure the exponential Lagrange stability for delayed-impulses in discrete-time CGNNs. Meanwhile, the exponential convergent domain for network is given. Finally, some examples with their simulations are given to verify the effectiveness of our results.

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Acknowledgements

The authors are grateful for the Youth Fund of Chongqing Three Gorges University (Grant No. 16QN14), and the support of the National Natural Science Foundation of China (11601047), Project Supported by Chongqing Municipal Key Laboratory of Institutions of Higher Education (Grant No. [2017]3).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Chongqing Three Gorges University, Chongqing, 404100, China

    Wenlin Jiang, Liangliang Li & Zhengwen Tu

  2. Key Laboratory of Intelligent information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China

    Liangliang Li & Yuming Feng

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  1. Wenlin Jiang
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  2. Liangliang Li
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  4. Yuming Feng
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Correspondence to Liangliang Li.

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Jiang, W., Li, L., Tu, Z. et al. Lagrange Stability for Delayed-Impulses in Discrete-Time Cohen–Grossberg Neural Networks with Delays. Neural Process Lett 51, 1835–1848 (2020). https://doi.org/10.1007/s11063-020-10190-2

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