REDES NEURONALES ARTIFICIALES: MODELADO, ENTRENAMIENTO Y VALIDACIÓN
DOI:
https://doi.org/10.32399/icuap.rdic.2448-5829.2026.35.1728Palabras clave:
Redes neuronales artíficiales, Entrenamiento, Validación, PruebaResumen
El documento aborda de manera integral el concepto, funcionamiento y aplicación de las Redes Neuronales Artificiales (RNA), destacándose como una herramienta fundamental de la inteligencia artificial inspirada en el sistema nervioso biológico. Las RNA tienen la capacidad de aprender a partir de datos, reconocer patrones complejos y apoyar la toma de decisiones, lo que ha impulsado su uso en diversos sectores. Se describen sus principales aplicaciones en áreas como salud y biotecnología, industria y automatización, alimentos y biotecnología alimentaria, tecnologías de la información, finanzas y educación, resaltando su utilidad en diagnóstico, predicción, optimización de procesos y análisis de grandes volúmenes de datos. El documento explica cómo funciona un RNA, detallando el modelo de neurona artificial basado en entradas, pesos sinápticos, suma ponderada y funciones de activación. Asimismo, se presentan los tipos de aprendizaje automático: supervisado, no supervisado y por refuerzo, señalando sus características, ventajas y limitaciones.Como conclusión, se enfatiza que las RNA son sistemas de procesamiento de información altamente versátiles, cuya principal fortaleza es su capacidad de aprendizaje a partir de datos mediante el ajuste de sus conexiones internas.
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