具有时间延迟和不连续激活的分数阶复值 BAM 神经网络的准同步化

Libo Wang, Guigui Xu
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摘要

本文探讨了一类具有时间延迟、不连续激活函数和不确定性的分数阶复值 BAM 神经网络(FCBAMNN)的准同步问题。首先,利用拉普拉斯变换和 Mittag-Leffler 函数特性,推导出一种新的分数微分不等式。然后,通过不可分解方法,得到了确保所考虑的 FCBAMNNs 准同步的充分条件。此外,还明确评估了同步的误差边界。最后,提供了一个数值示例来验证所提出的结果。
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Quasi-synchronization of fractional-order complex-value BAM neural networks with time delays and discontinuous activations

This paper explores quasi-synchronization for a class of fractional-order complex-valued BAM neural networks (FCBAMNNs) with time delays, discontinuous activation functions, and uncertainties. Firstly, by utilizing Laplace transform and the Mittag–Leffler function property, a novel fractional differential inequality is derived. Then, sufficient conditions are obtained to ensure the quasi-synchronization for the considered FCBAMNNs by means of non-decomposable method. Additionally, the error bound of synchronization is explicitly evaluated. Finally, a numerical example is provided to validate the proposed results.

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11.50%
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352
期刊介绍: Computational & Applied Mathematics began to be published in 1981. This journal was conceived as the main scientific publication of SBMAC (Brazilian Society of Computational and Applied Mathematics). The objective of the journal is the publication of original research in Applied and Computational Mathematics, with interfaces in Physics, Engineering, Chemistry, Biology, Operations Research, Statistics, Social Sciences and Economy. The journal has the usual quality standards of scientific international journals and we aim high level of contributions in terms of originality, depth and relevance.
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