时变时滞分数阶神经网络s -渐近ω-周期解的多重稳定性

Chenxi Song, Sitian Qin, Jiqiang Feng
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引用次数: 0

摘要

本文研究了具有时变时滞的分数阶神经网络(FVDNNs)的s -渐近周期解的多重稳定性。利用非线性非单调激活函数的几何构型,证明了具有多重渐近稳定性的$(K+1)^{n}$ s -周期解的共存性,其中K为正整数。与以往的工作相比,本文得到的结果广泛地提高了fvdnn的s -渐近$\ ω $周期解的数量。并通过两个数值算例说明了所得结果的可行性。
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Multistability of S-Asymptotically ω-Periodic Solutions for Fractional-Order Neural Networks with Time Variable Delays
This paper explores the multistability of S-asymptotically $\omega$-periodic solutions for fractional-order neural networks with time variable delays (FVDNNs). Benefited from the geometrical configuration of the nonlinear and non-monotonic activation function, we prove the coexistence of $(K+1)^{n}$ S-asymptotically $\omega$-periodic solutions with multiple asymptotical stability, where K is a positive integer. In contrast to the previous works, the obtained results extensively raise the amount of S-asymptotically $\omega$-periodic solutions of FVDNNs in this paper. Besides, two numerical examples are shown to illustrate the feasibility of obtained results.
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