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Self-Discrepancy Theory02:45

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Self-Discrepancy and Its Effects01:29

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Self-discrepancy theory explains how people compare their actual self to their ideal and ought selves and how mismatches between these self-guides can lead to emotional distress. Developed by E. Tory Higgins, the theory distinguishes among three components of self-concept: the actual self, the ideal self, and the ought self. These refer respectively to how individuals perceive themselves, how they aspire to be, and how they believe they are obligated to be. Emotional well-being, self-esteem,...
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This video article illustrates the set-up, the procedures to patch cell bodies and how to implement dynamic clamp recordings from ganglion cells in whole-mount mouse retinae. This technique allows the investigation of the precise contribution of excitatory and inhibitory synaptic inputs, and their relative magnitude and timing to neuronal...
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Video Experimental Relacionado

Updated: Jan 20, 2026

Self-Discrepancy Theory: Actual Self, Ideal Self and Ought Self
02:45

Self-Discrepancy Theory: Actual Self, Ideal Self and Ought Self

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Un marco para la implementación de modelado de orden reducido con discrepancia dinámica en control avanzado de

San Dinh1, Claudemi A Nascimento1, David S Mebane2

  • 1Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, West Virginia 26506, United States.

Industrial & engineering chemistry research
|January 19, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo enfoque de modelado de caja gris para el control avanzado de procesos. Mejora la precisión del modelo y la eficiencia computacional para el modelado de orden reducido con discrepancia dinámica en el control predictivo de modelos (MPC).

Palabras clave:
modelado de orden reducido con discrepancia dinámicacontrol avanzado de procesoscontrol predictivo de modelosmodelado de caja grisingeniería de reactores químicos

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Área de la Ciencia:

  • Ingeniería Química
  • Ingeniería de Sistemas de Control
  • Modelado Computacional

Sus antecedentes:

  • El control avanzado de procesos se basa en modelos dinámicos precisos.
  • Los modelos de orden reducido ofrecen eficiencia computacional pero a menudo sacrifican precisión.
  • Los métodos existentes para la corrección de desajustes de modelos son limitados.

Objetivo del estudio:

  • Presentar un marco novedoso de modelado de orden reducido con discrepancia dinámica.
  • Mejorar la precisión del modelo y el rendimiento computacional en el control de procesos.
  • Desarrollar un enfoque de modelado de caja gris para aplicaciones de control avanzado de procesos.

Principales métodos:

  • Construcción de modelos de caja gris que combinan componentes de primeros principios y de caja negra.
  • Enfoque en las discrepancias en las tasas de cambio dentro del modelo de orden reducido.
  • Utilización de la estimación de horizonte móvil para la generación de datos y la inferencia bayesiana para la calibración.

Principales resultados:

  • El enfoque de discrepancia dinámica propuesto compensa la información dinámica perdida en los modelos reducidos.
  • Se demostró una mejora en la precisión del modelo y el rendimiento computacional.
  • El marco se validó utilizando una simulación de reactor de síntesis Fischer-Tropsch.

Conclusiones:

  • El marco novedoso implementa eficazmente el modelado de orden reducido con discrepancia dinámica.
  • Este enfoque mejora la idoneidad de los modelos de orden reducido para el control predictivo de modelos (MPC).
  • El método ofrece un equilibrio entre la complejidad computacional y la precisión del modelo para el control avanzado de procesos.