Abstract
In this work, the stability analysis of a class of conformable fractional-order Hopfield neural networks is investigated. By using the Lyapunov function and M-matrix method, certain novel results on the existence, uniqueness and fractional exponential stability of the equilibrium point have been established. The derived criteria improve and extend some recent results in the literature. Finally, the advantage of our theoretical results is illustrated via a numerical example.
This research is partially supported by the Research Education-funded Projects in Yunnan Province (2021J0713), the Natural Science Foundation of Yunnan Provincial Department of Science and Technology (202101BA070001-132) and the Research Fund of Kunming University (YJL17004, YJL20015, YJL20019).
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Yang, Cb., Hong, Sy., Li, Yq., Wang, Hm., Zhu, Y. (2022). Stability Analysis of Hopfield Neural Networks with Conformable Fractional Derivative: M-matrix Method. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_13
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