Predicting failures in fiber optic information transmission systems with support of machine learning

Nafisa Juraeva; Dilmurod Davronbekov; Ulugbek Turdiev

doi:10.51252/rcsi.v5i2.907

Autores/as

Nafisa Juraeva Tashkent University of Information Technology
Dilmurod Davronbekov Tashkent University of Information Technology
Ulugbek Turdiev University of Information Technologies and Management

DOI:

https://doi.org/10.51252/rcsi.v5i2.907

Palabras clave:

algoritmos de regresión, aprendizaje automático, bosque aleatorio, predicción de fallos, regresión de vectores de soporte, regresor de árbol extra

Resumen

Se considera el uso de métodos de aprendizaje automático en sistemas de transmisión de información por fibra óptica (FOITS). El artículo analiza los principios básicos de funcionamiento de los sistemas de fibra óptica y los problemas que enfrentan, como el ruido, los efectos no lineales y la degradación de la información transmitida. Describe diversas técnicas de aprendizaje automático utilizadas en FOITS para controlar y supervisar el rendimiento, prevenir decisiones inteligentes y suprimir el ruido no lineal en la fibra óptica. Se presentan enfoques utilizados en aprendizaje automático, como redes neuronales, algoritmos de clasificación y regresión, y su aplicación en el análisis y la optimización de FOITS. Este artículo propone un método para supervisar el rendimiento y predecir fallos en redes ópticas basado en aprendizaje automático. Los resultados obtenidos permiten extraer conclusiones sobre los métodos más eficaces para predecir fallos, lo cual es de gran importancia práctica para garantizar la fiabilidad de las redes de comunicación y minimizar el tiempo de inactividad.

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