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Estabilidad generalizada y memoria asociativa de redes neuronales recurrentes retardadas con entrada externa variable
Fanghai Zhang1, Peng Gao1, Tingwen Huang2
1School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009 China.
Cognitive neurodynamics
|August 22, 2025
Resumen
Este estudio investiga la estabilidad y la memoria asociativa de las redes neuronales recurrentes retrasadas. Los investigadores mejoraron la capacidad de almacenamiento y diseñaron una memoria asociativa de alta capacidad utilizando nuevas condiciones de estabilidad.
Área de la Ciencia:
- Neurociencia computacional
- Inteligencia artificial
- Sistemas dinámicos
Sus antecedentes:
- Las redes neuronales de retraso recurrente (DRNNs) son cruciales para el procesamiento de información compleja.
- Comprender su estabilidad y capacidad de memoria es esencial para aplicaciones avanzadas.
- Los modelos existentes a menudo se enfrentan a limitaciones en el manejo de entradas variables y la maximización del almacenamiento.
Objetivo del estudio:
- Investigar la estabilidad generalizada de las DRNN con insumos externos variables.
- Analizar la coexistencia y la estabilidad de múltiples puntos de equilibrio.
- Para diseñar un sistema de memoria asociativa de alta capacidad.
Principales métodos:
- Aplicación del principio de comparación para establecer la monostabilidad.
- Extensión de los conceptos de estabilidad a las DRNN con entradas variables.
- Modificación de las funciones de activación para aumentar los puntos de equilibrio estables.
- Derivación de las condiciones suficientes para la estabilidad generalizada.
Principales resultados:
- Monostabilidad de sistemas diferenciables normales establecida y extendida a DRNNs.
- Análisis de múltiples puntos de equilibrio y mejora de la capacidad de almacenamiento.
- Desarrollo de condiciones para la estabilidad generalizada, que abarca la estabilidad exponencial.
- Diseño exitoso de una memoria asociativa de alta capacidad basada en patrones bipolares estables.
Conclusiones:
- El estudio proporciona un marco teórico para analizar y mejorar la estabilidad y la memoria de las DRNNs.
- Los métodos propuestos aumentan significativamente la capacidad de almacenamiento de la memoria asociativa.
- Los ejemplos numéricos validan los hallazgos teóricos y el diseño práctico de la memoria asociativa.
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