Tuning up Fuzzy Inference Systems by using optimization algorithms for the classification of solar flares
Liz Ang\'elica Ramos Medina (1), Alex Francisco Bustos Pinz\'on (1),, Miguel A. Melgarejo (1), Santiago Vargas Dom\'inguez (2) ((1) Universidad, Distrital Francisco Jos\'e de Caldas, Bogot\'a, Colombia (2) OAN -, Universidad Nacional de Colombia, Bogot\'a, Colombia)
TL;DR
This paper explores the use of various optimization algorithms to tune fuzzy inference systems for improved solar flare classification accuracy.
Contribution
It introduces a methodology for optimizing fuzzy inference system parameters specifically for solar flare classification tasks.
Findings
Optimization algorithms effectively improve classification accuracy.
Parameter tuning enhances fuzzy system performance.
The approach is applicable to other classification problems.
Abstract
In this work we describe the implementation and analysis of different optimization algorithms used for finding the best set of parameters for a Fuzzy Inference System intended to classify solar flares. The parameters will be identified among a universe of possible solutions for the algorithms, and the system will be tested in the particular case of dealing with the aim of classifying the solar flares.
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Taxonomy
TopicsSolar Radiation and Photovoltaics · Solar and Space Plasma Dynamics · Educational Methods and Technology