Exploration of Parameter Space in the Lorenz Attractor

This study investigates the behavioral variations of the Lorenz attractor under different system parameter settings, aiming to elucidate how these parameters influence the dynamic characteristics of chaotic systems. By systematically altering the three key parameters in the Lorenz system—ρ, β, σ, —we examine the evolution of bifurcation diagrams and phase-space trajectories. The results reveal that as parameters are adjusted, the Lorenz system transitions from a stable state to a complex chaotic attractor, exhibiting prominent bifurcations and chaotic features. Furthermore, numerical simulations illustrate the system's sensitivity to initial conditions, offering deeper insights into the dynamic behavior of chaotic systems. This work provides a theoretical foundation for understanding the complexity inherent in nonlinear dynamical systems.

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Publication Details

Journal
Mathematical Modeling and Algorithm Application
Published
2024-11-28
DOI
https://doi.org/10.54097/tw5qvr32
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

Exploration of Parameter Space in the Lorenz Attractor

Kun Yang, Xiao Liao
Mathematical Modeling and Algorithm Application
Neural Networks and Applications
article

Exploration of Parameter Space in the Lorenz Attractor

Kun Yang, Xiao Liao
article en

Abstract

This study investigates the behavioral variations of the Lorenz attractor under different system parameter settings, aiming to elucidate how these parameters influence the dynamic characteristics of chaotic systems. By systematically altering the three key parameters in the Lorenz system—ρ, β, σ, —we examine the evolution of bifurcation diagrams and phase-space trajectories. The results reveal that as parameters are adjusted, the Lorenz system transitions from a stable state to a complex chaotic attractor, exhibiting prominent bifurcations and chaotic features. Furthermore, numerical simulations illustrate the system's sensitivity to initial conditions, offering deeper insights into the dynamic behavior of chaotic systems. This work provides a theoretical foundation for understanding the complexity inherent in nonlinear dynamical systems.

Mathematical Modeling and Algorithm ApplicationVol. 3(2)
Openalex Percentile: Top 80%
Neural Networks and Applications
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