基于自适应自同步的混沌系统建模 - 预测混沌的稳定性和可控性
Year: 2026
Type: preprint
Abstract: This paper introduces a novel approach to chaotic system modeling, leveraging the concept of self-adaptive synchronization (SAS) to enhance prediction accuracy and stability. Traditional methods often rely on static parameter adjustments, which limit the potential for precise forecasting. We present a new algorithm that dynamically adjusts the system's parameters during the modeling process, allowing for a more refined and adaptive representation of chaotic dynamics. The proposed method demonstrates improved predictive capabilities and offers a pathway towards more stable and controllable chaos. This research focuses on developing a robust framework for understanding and quantifying the interplay between system parameters and chaotic behavior, ultimately providing a significant advancement in the field of chaos theory.
Author: Zhang, Jincheng
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7208772983