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(*) UNIVERSIDAD MICHOACANA DE SAN NICOLÁS DE HIDALGO FACULTAD DE INGENIERÍA ELÉCTRICA DIVISIÓN DE ESTUDIOS DE POSGRADO META-MODELADO DE PREDICTORES DE SERIES DE TIEMPO No. Hrs. /Semana

:

Duración en semanas: Tema

4 16

Total de Horas: Número de Créditos:

64 8

Conocimientos previos recomendados:

Introducción a aprendizaje supervisado

Objetivo

: Que el alumno conozca la metodología tradicional que ha sido utilizadas para resolver el problema de la selección de un algoritmo en el problema de la predicción de series de tiempo en específico de la velocidad del viento.

Programa sintético: Duración (hrs.)

1.

2.

3.

4.

5.

Introducción Ecuaciones Auto-regresivas Redes Neuronales Problema de la Selección de un Algoritmo Indicadores de Dificultad de Problemas 6.

7.

8.

9.

Modelado Portafolio de Algoritmos Taxonomía de Algoritmos Análisis de Modelos 10.

Conclusiones 2 8 4 4 8 8 8 8 8 6

Total de Horas Programa desarrollado:

1.

Introducción 1.1.

El problema de la selección de un algoritmo 1.2.

Series de tiempo 1.3.

Técnicas para predecir la velocidad del viento 2.

Ecuaciones Auto-regresivas 2.1.

Introducción 2.2.

Mínimos cuadrados 2.3.

LARS 2.4.

LASSO 2.5.

Técnicas para sintonizar la ventana de observaciones 3.

Redes Neuronales

64

3.1.

Introducción 3.2.

Similitud de redes neuronales y ecuaciones auto-regresivas 3.3.

Pasos de predicción 3.4.

Redes neuronales recurrentes

4.

Problema de la Selección de un Algoritmo 4.1.

Introducción 4.2.

Metodología 4.3.

Listado del estado del arte 5.

Indicadores de Dificultad de Problemas 5.1.

Introducción 5.2.

Indicador de Franco 5.3.

Modificaciones al indicador de Franco 5.4.

Indicadores usados en trabajos relacionados 5.5.

Indicadores usados en otros dominios 6.

Modelado 6.1.

Introducción 6.2.

Modelos usados en trabajo relacionado 6.3.

Modelos usados en algoritmos evolutivos 6.4.

Modelos en otros dominios 7.

Portafolio de Algoritmos 7.1.

Introducción 7.2.

Creación de un portafolio 7.3.

Análisis del portafolio Taxonomía de Algoritmos 8.

8.1.

Introducción 8.2.

Taxonomía de Programación Genética 8.3.

Metodología usada en su construcción. 8.4.

Creación de una taxonomía Análisis de Modelos 9.

9.1.

Introducción 9.2.

Análisis realizado en los modelos de programación genética 9.3.

Analizar los modelos creados 10.

Conclusiones

Bibliografía:

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Metodología de enseñanza-aprendizaje:

Revisión de conceptos, análisis y solución de problemas en clase Lectura de material fuera de clase Ejercicios fuera de clase (tareas) X X X X X Investigación documental Elaboración de reportes técnicos o proyectos

Metodología de evaluación:

Asistencia Tareas Elaboración de reportes técnicos o proyectos Exámenes

Programa propuesto por:

Mario Graff Guerrero

Fecha de aprobación:

23 de marzo de 2012 X X

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