Effect of fear in a fractional order prey–predator model with time delayed carrying capacity

Q3 Mathematics
Pramodh Bharati , Subrata Paul , Animesh Mahata , Supriya Mukherjee , Subhabrata Mondal , Banamali Roy
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引用次数: 0

Abstract

The Caputo technique is used in this article to analyze the fractional-order predator–prey scenario. Incorporating a delayed carrying capacity for the prey population and posing the impact of individual prey fear on predators are two aspects of this. We first provide the model’s formulation in terms of an integer order derivative, and subsequently we expand it to a fractional order system in terms of the Caputo derivative. The article contains a number of conclusions about the prerequisites for the model’s existence and uniqueness as well as the restrictions on the boundedness and positivity of the solution. To satisfy the requirements for the existence and uniqueness of the precise solution, the Lipschitz condition is applied. Within the local context, we have examined the stability of equilibrium points. Additionally, we investigated whether Hopf bifurcation may occur at the interior equilibrium point of our suggested model. We have used the Generalised Euler technique to approximatively solve the model. The suggested scheme’s dependability is indicated by the fact that the results produced using the current numerical approach converge to equilibrium for the fractional order. For our research, MATLAB was used to enable graphical representations and numerical simulations.
具有时间延迟承载能力的分数阶捕食模型中恐惧的影响
本文使用Caputo技术来分析分数阶捕食者-猎物场景。将猎物种群的延迟承载能力与单个猎物对捕食者的恐惧的影响结合起来是这方面的两个方面。我们首先提供了一个整数阶导数的模型公式,随后我们将其扩展到一个分数阶系统的Caputo导数。本文给出了模型存在唯一性的先决条件以及解的有界性和正性的限制条件。为了满足精确解的存在唯一性要求,应用了Lipschitz条件。在局部情况下,我们考察了平衡点的稳定性。此外,我们还研究了Hopf分岔是否可能发生在我们建议的模型的内部平衡点上。我们使用广义欧拉技术对模型进行了近似求解。采用现有数值方法得到的结果收敛于分数阶的平衡态,表明了所提方案的可靠性。在我们的研究中,使用MATLAB进行图形表示和数值模拟。
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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来源期刊
Results in Control and Optimization
Results in Control and Optimization Mathematics-Control and Optimization
CiteScore
3.00
自引率
0.00%
发文量
51
审稿时长
91 days
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