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Global Lagrange Stability of Inertial Neutral Type Neural Networks with Mixed Time-Varying Delays

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Abstract

This paper deals with the Lagrange stability of inertial neutral type neural networks with mixed time-varying delays. Two different types of activation functions are considered, including bounded and general unbounded activation functions. Under a proper variable transformation, the original inertial system is converted to a first order differential network. Based on Lyapunov method and applying inequality techniques and analytical method, some sufficient criteria are derived to ensure the global Lagrange exponential stability of the addressed neural networks. Moreover, the global exponential attractive sets are established. These results here generalize and improve the earlier publications on inertial neural networks. Finally, some numerical examples with simulations are given to demonstrate the effectiveness of our theoretical results.

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Authors and Affiliations

  1. College of Science, China Three Gorges University, Yichang, 443002, Hubei, China

    Liyan Duan & Jigui Jian

  2. Three Gorges Mathematical Research Center, China Three Gorges University, Yichang, 443002, Hubei, China

    Jigui Jian

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  1. Liyan Duan
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  2. Jigui Jian
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Correspondence to Jigui Jian.

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Duan, L., Jian, J. Global Lagrange Stability of Inertial Neutral Type Neural Networks with Mixed Time-Varying Delays. Neural Process Lett 51, 1849–1867 (2020). https://doi.org/10.1007/s11063-019-10177-8

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  • DOI: https://doi.org/10.1007/s11063-019-10177-8

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