通过新函数方法为时延分数阶神经网络设计采样数据控制器

N. Padmaja
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摘要

本手稿的重点在于为具有 Lipschitz 非线性的混合分数阶神经网络(FONN)引入一种新的 Lyapunov-Krasovskii 函数(LKF)。这项工作的主要独创性在于探索使用类似于循环函数的新型函数来分析具有延迟的混合分数阶系统(FOS)稳定性的可能性。虽然在这个方向上已经有了一些尝试,但这个概念的正式理论还没有发展起来。首先,利用任意环状 LKF 和分数阶 Lyapunov 直接法推导出了一个关于建立渐近稳定性的新公理。利用这一结果,以 LMI 的形式为所考虑的 FONN 建立了新的延迟和采样周期相关稳定性准则。最后,数值模拟验证了本手稿中提出的理论结果的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Designing sampled data controller for time-delayed fractional-order neural networks via a new functional approach

The main focus of this manuscript is vested in the introduction of a new Lyapunov–Krasovskii functional (LKF) for hybrid fractional-order neural networks (FONNs) with Lipschitz non-linearity. The primary originality of this work lies in exploring the possibility of using a new type of functionals similar to looped functional for the stability analysis of hybrid fractional-order systems (FOSs) with delays. Although some work in this direction has been attempted, the formal theory for this concept has not been developed yet. First, a new lemma on establishing asymptotic stability using an arbitrary looped-like LKF and the fractional-order Lyapunov direct method is derived. Using this result, new delay and sampling period dependent stability criteria for the considered FONNs are established in the form of LMIs. Lastly, numerical simulations validate the correctness of the theoretical results proposed in this manuscript.

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