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Redes de configuración estocástica recurrente con incrementos de bloques

Dianhui Wang1, Gang Dang2

  • 1School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China.

Neural networks : the official journal of the International Neural Network Society
|August 21, 2025
PubMed
Resumen

Las redes de configuración estocástica recurrente de bloques (BRSCN) mejoran el modelado de sistemas dinámicos no lineales mediante la adición de múltiples subreservorios. Este enfoque mejora la eficiencia del aprendizaje y la generalización para dinámicas complejas.

Palabras clave:
Incrementos en el bloquePropiedad del Estado de EchoEs una excitación persistente.Red de configuración estocástica recurrentePropiedad de aproximación universal

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Área de la Ciencia:

  • Neurociencia computacional
  • Aprendizaje automático
  • Dinámica no lineal

Sus antecedentes:

  • Las redes de configuración estocástica recurrente (RSCN) son efectivas para sistemas dinámicos no lineales con incertidumbre de orden.
  • Las RSCN existentes ofrecen facilidad de implementación, reducción de la intervención humana y fuertes capacidades de aproximación.

Objetivo del estudio:

  • Introducir redes de configuración estocástica recurrente de bloques (BRSCN) para mejorar la capacidad y la eficiencia del aprendizaje.
  • Mejorar el modelado de sistemas dinámicos complejos no lineales.

Principales métodos:

  • Desarrollar BRSCN capaces de añadir múltiples nodos de depósito (subdepósitos) al mismo tiempo.
  • Configurar cada subdepósito con estructuras únicas utilizando un mecanismo de supervisión.
  • Escalar la matriz de retroalimentación del depósito para garantizar la propiedad del estado de eco.
  • Emplear actualizaciones de peso de salida en línea a través de un algoritmo de proyección.
  • Establecer condiciones de excitación persistentes para la convergencia de los parámetros.

Principales resultados:

  • Los BRSCN demuestran una eficiencia de modelado y un rendimiento de aprendizaje superiores.
  • El método propuesto muestra un rendimiento de generalización favorable en varias tareas.
  • Eficacia validada en la predicción de series temporales, la identificación de sistemas no lineales y el análisis de datos industriales.

Conclusiones:

  • Los BRSCN ofrecen un potencial significativo para modelar dinámicas complejas con mayor eficiencia.
  • La nueva arquitectura mejora las RSCN tradicionales para el análisis dinámico de sistemas.
  • Los BRSCN proporcionan un marco sólido para abordar problemas difíciles de modelado no lineal.