New Methodology for the Differentiation of One-Dimensional Discrete Signals Through the Use of Geometric Features
DOI:
https://doi.org/10.70567/mc.v41i15.79Keywords:
amplitud, ángulo de zenit, factor de forma, diferenciación de señalesAbstract
This study focuses on the application of an algorithm for the classification of chaotic dynamical
systems, including the Henón, Chirikov, Schuster, Logistic, and Ricker population model systems.
The algorithm employs the sweep technique as its main mechanism, for which an embedding dimension
and a discretization parameter are defined. Classification of chaotic dynamical systems is carried
out by extracting geometric features. The methodology utilizes three elements: signal amplitude, Zenith
angle, and a shape factor, generating triplets and reducing those that repeat during the process. From
these data, a feature space is created where each vector is unique. In addition to classification, a detailed
analysis of the space generated by the algorithm is performed, which provides additional information
complementing the classification performed.
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