Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

El control de procesos con dinámica estocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al comportamiento, sin embargo, esta suposición pierde validez en aplicaciones donde la información del sistema es reducida o incompleta, muy comunes en ambientes real...

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Published inIteckne Vol. 10; no. 1; pp. 113 - 127
Main Authors Dávila Gómez, Amalia, Peña Palacio, Alejandro, Ortiz Valencia, Paula Andrea, Delgado Trejos, Edilson
Format Journal Article
LanguageEnglish
Portuguese
Spanish
Published Universidad Santo Tomás 01.06.2013
Subjects
Control adaptativo
control adaptativo, sistemas estocás- ticos, redes neuronales, lógica difusa, modelos neuro- difusos, información incompleta
difusos
ENGINEERING, MULTIDISCIPLINARY
información incompleta
lógica difusa
modelos neuro
redes neuronales
sistemas estocás
ticos
incomplete system data
neuro-fuzzy models
sistemas estocásticos
fuzzy Logic
Control adaptativo
neural networks
información incompleta
stochastic systems
redes neuronales
Adaptive control
lógica difusa
modelos neurodifusos
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Abstract El control de procesos con dinámica estocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al comportamiento, sin embargo, esta suposición pierde validez en aplicaciones donde la información del sistema es reducida o incompleta, muy comunes en ambientes reales de la industria. La literatura presenta diferentes esquemas de control, siendo los modelos neuro-difusos los que reportan mejor desempeño. Estos modelos conjugan la capacidad de adaptación que tienen las redes neuronales con la robustez de los motores de inferencia que tiene la lógica difusa, para modelar el conocimiento de expertos mediante reglas de aprendizaje, identificar dinámicas complejas y aumentar la adaptabilidad del sistema a perturbaciones que en la práctica tienden a ser de naturaleza estocástica sumado, a veces, que la información del sistema sea restringida. Este artículo presenta una revisión sobre dificultades y soluciones derivadas del control de sistemas estocásticos o complejos con información incompleta. Se revisan las estructuras de control cuando la dinámica del sistema presenta vaguedad en los datos, la evolución hacia técnicas adaptativas, y el desempeño de las redes neuro-difusas ante procesos estocásticos o complejos con incertidumbre en los datos. De forma preliminar se establece que el control de este tipo de sistemas debe estar compuesto por modelos híbridos soportados en rutinas de optimización y análisis probabilístico que garanticen el tratamiento de las incertidumbres sin afectar el desempeño de las estructuras de control y la consistencia en la precisión.
AbstractList El control de procesos con dinámica es- tocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al compor- tamiento, sin embargo, esta suposición pierde validez en aplicaciones donde la información del sistema es reducida o incompleta, muy comunes en ambientes reales de la industria. La literatura presenta diferentes esquemas de control, siendo los modelos neuro-difusos los que reportan mejor desempeño. Estos modelos con- jugan la capacidad de adaptación que tienen las redes neuronales con la robustez de los motores de inferencia que tiene la lógica difusa, para modelar el conocimien- to de expertos mediante reglas de aprendizaje, identifi- car dinámicas complejas y aumentar la adaptabilidad del sistema a perturbaciones que en la práctica tien- den a ser de naturaleza estocástica sumado, a veces, que la información del sistema sea restringida. Este artículo presenta una revisión sobre dificultades y solu- ciones derivadas del control de sistemas estocásticos o complejos con información incompleta. Se revisan las estructuras de control cuando la dinámica del sis- tema presenta vaguedad en los datos, la evolución ha- cia técnicas adaptativas, y el desempeño de las redes neuro-difusas ante procesos estocásticos o complejos con incertidumbre en los datos. De forma preliminar se establece que el control de este tipo de sistemas debe estar compuesto por modelos híbridos soportados en rutinas de optimización y análisis probabilístico que garanticen el tratamiento de las incertidumbres sin afectar el desempeño de las estructuras de control y la consistencia en la precisión.
El control de procesos con dinámica estocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al comportamiento, sin embargo, esta suposición pierde validez en aplicaciones donde la información del sistema es reducida o incompleta, muy comunes en ambientes reales de la industria. La literatura presenta diferentes esquemas de control, siendo los modelos neuro-difusos los que reportan mejor desempeño. Estos modelos conjugan la capacidad de adaptación que tienen las redes neuronales con la robustez de los motores de inferencia que tiene la lógica difusa, para modelar el conocimiento de expertos mediante reglas de aprendizaje, identificar dinámicas complejas y aumentar la adaptabilidad del sistema a perturbaciones que en la práctica tienden a ser de naturaleza estocástica sumado, a veces, que la información del sistema sea restringida. Este artículo presenta una revisión sobre dificultades y soluciones derivadas del control de sistemas estocásticos o complejos con información incompleta. Se revisan las estructuras de control cuando la dinámica del sistema presenta vaguedad en los datos, la evolución hacia técnicas adaptativas, y el desempeño de las redes neuro-difusas ante procesos estocásticos o complejos con incertidumbre en los datos. De forma preliminar se establece que el control de este tipo de sistemas debe estar compuesto por modelos híbridos soportados en rutinas de optimización y análisis probabilístico que garanticen el tratamiento de las incertidumbres sin afectar el desempeño de las estructuras de control y la consistencia en la precisión.
Author Dávila Gómez, Amalia
Ortiz Valencia, Paula Andrea
Delgado Trejos, Edilson
Peña Palacio, Alejandro
AuthorAffiliation Escuela de Ingeniería de Antioquia
Fundación Universitaria. Luis Amigó - FUNLAM
Instituto Tecnológico Metropolitano ITM
AuthorAffiliation_xml – name: Escuela de Ingeniería de Antioquia
– name: Instituto Tecnológico Metropolitano ITM
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  surname: Dávila Gómez
  fullname: Dávila Gómez, Amalia
  organization: Fundación Universitaria. Luis Amigó - FUNLAM
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  givenname: Alejandro
  surname: Peña Palacio
  fullname: Peña Palacio, Alejandro
  organization: Escuela de Ingeniería de Antioquia
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  givenname: Paula Andrea
  surname: Ortiz Valencia
  fullname: Ortiz Valencia, Paula Andrea
  organization: Instituto Tecnológico Metropolitano ITM
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  givenname: Edilson
  surname: Delgado Trejos
  fullname: Delgado Trejos, Edilson
  organization: Instituto Tecnológico Metropolitano ITM
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Issue 1
Keywords incomplete system data
neuro-fuzzy models
sistemas estocásticos
fuzzy Logic
Control adaptativo
neural networks
información incompleta
stochastic systems
redes neuronales
Adaptive control
lógica difusa
modelos neurodifusos
Language English
Portuguese
Spanish
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Snippet El control de procesos con dinámica estocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al comportamiento, sin...
El control de procesos con dinámica es- tocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al compor- tamiento, sin...
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SubjectTerms Control adaptativo
control adaptativo, sistemas estocás- ticos, redes neuronales, lógica difusa, modelos neuro- difusos, información incompleta
difusos
ENGINEERING, MULTIDISCIPLINARY
información incompleta
lógica difusa
modelos neuro
redes neuronales
sistemas estocás
ticos
Title Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control
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