合成logistic -高斯映射的动力学性质。

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-01-01 DOI:10.1063/5.0238591
Luam Silva de Paiva, Julia G S Rocha, Joelson D V Hermes, Matheus Hansen, Ricardo Luiz Viana, Iberê Luiz Caldas, Rene O Medrano-T, Diogo Ricardo da Costa
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引用次数: 0

摘要

本研究的重点是分析两个成熟模型之间的独特组成,即Logistic-Gauss图。研究紧密地过渡到对参数空间的探索,这对于揭示耗散映射的复杂性和理解周期结构和混沌区域之间的复杂关系至关重要。通过操纵控制参数,我们的方法揭示了有趣的模式,并通过极端轨道(连接一维地图的局部最大值和最小值的轨迹)丰富了研究结果。该理论增强了我们对结构组织的感知,并提供了对系统行为的有价值的感知,有助于扩大对动态系统中的混沌和周期性的理解。分析揭示了参数空间中的复周期集(CSP),其特征是超稳定曲线穿越其主体。对不同参数组合的探索显示了具有附加周期的CSP结构级联,并基于极端曲线组织。这项研究提供了耗散映射动力学的有价值的发现,为混沌系统的未来探索开辟了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamical properties of the composed Logistic-Gauss map.

This study focuses on the analysis of a unique composition between two well-established models, known as the Logistic-Gauss map. The investigation cohesively transitions to an exploration of parameter space, essential for unraveling the complexity of dissipative mappings and understanding the intricate relationships between periodic structures and chaotic regions. By manipulating control parameters, our approach reveals intriguing patterns, with findings enriched by extreme orbits, trajectories that connect local maximum and minimum values of one-dimensional maps. This theory enhances our perception of structural organization and offers valuable perceptions of the system behaviors, contributing to an expanded understanding of chaos and periodicity in dynamic systems. The analysis reveals Complex Sets of Periodicity (CSP) in the parameter space, characterized by superstable curves that traverse their main bodies. The exploration of different combinations of parameters shows cascades of CSP structures with added periods and are organized based on extreme curves. This investigation offers valuable discoveries of the dynamics of dissipative mappings, opening avenues for future explorations in chaotic systems.

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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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