Characterizing Nonlinear Dynamics of Logistic Map Using Machine Learning: A Coarse Approach
Year: 2025
Type: conference-paper
Abstract: Identifying nonlinear dynamic systems that often exhibit chaotic behavior can be a complex task. Recognizing chaotic behavior in data can provide valuable insights for its utilization. Mathematically, the identification of chaotic behavior in data typically requires the consideration of multiple parameters. However, nonlinear dynamic systems can be readily identified using machine learning. In this study, a machine learning model was constructed using a deterministic dataset generated from logistic map equations, employing the Long Short-Term Memory (LSTM) architecture. The outcomes of the machine learning model take the form of data classification, distinguishing between predictable and unpredictable data with quite high validity.
Source: Engineering headway
DOI: 10.4028/p-ibm8rn
Institution: Universitas Gadjah Mada
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415117305