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Positivity and global stability preserving NSFD schemes for a mixing propagation model of computer viruses

机译:阳性和全局稳定性保留计算机病毒混合传播模型的NSFD方案

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In this work, nonstandard finite difference (NSFD) schemes preserving the essential qualitative properties including positivity and stability of a mixing propagation model of computer viruses are proposed and analyzed for the first time. Especially, the model under consideration possesses the equilibrium points which are not only locally asymptotically stable but also globally asymptotically stable. Because of this reason we propose a new approach to prove theoretically that the global asymptotic stability of the original model is preserved by the proposed NSFD schemes. This approach is based on the Lyapunov stability theorem and its extension in combination with a theorem on the global stability of discrete-time nonlinear cascade systems. The important result is that we obtain NSFD schemes which are dynamically consistent with the continuous model. Some numerical examples are performed to support the theoretical results. The results show that there is a good agreement between the numerical simulations and the established theoretical results. Furthermore, the numerical simulations also indicate that the proposed NSFD schemes are suitable and effective to solve the continuous model, whereas, the standard numerical schemes such as the Euler scheme, the classical fourth order Runge-Kutta scheme are not dynamically consistent with the continuous model and consequently, they fail to reflect the correct behavior of the continuous model. (C) 2020 Elsevier B.V. All rights reserved.
机译:在这项工作中,第一次提出并分析了保持基本定性特性的非标准有限差异(NSFD)方案,包括计算机病毒的混合传播模型的积极性和稳定性。特别是,所考虑的模型具有均衡点,其不仅是局部渐近稳定的,而且是全球渐近稳定的。由于这个原因,我们提出了一种新的方法,从理论上证明了原始模型的全球渐近稳定性被提议的NSFD方案保留。这种方法基于Lyapunov稳定性定理及其扩展与定理与离散时间非线性级联系统的全局稳定性的定理结合。重要结果是我们获得了与连续模型动态一致的NSFD方案。执行一些数值示例以支持理论结果。结果表明,数值模拟与建立的理论结果之间存在良好的一致性。此外,数值模拟还表明,所提出的NSFD方案是合适的且有效解决连续模型,而诸如欧拉方案的标准数值方案,则经典的第四顺序runge-Kutta方案与连续模型不动态一致因此,它们无法反映连续模型的正确行为。 (c)2020 Elsevier B.v.保留所有权利。

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