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JOURNAL BOLIVIANO DE CIENCIAS – Vol. 20 – Número 55
ISSN Digital: 2075-8944 ISSN Impreso: 2075-8936
12
Para validar el modelo, se presenta a continuación una tabla que compara los datos
originales con los generados por el modelo. Adicionalmente, se calcula porcentaje
de error para las dos variables estimadas: el voltaje de salida y la fase.
DATASHEET MODELO
vin vout fase vout fase
0,14125375 6,30957344 -0,00785398 6,27018834 -0,00783111
0,17782794 7,76247117 0,003490659 7,74928015 0,00429459
0,19952623 8,51138038 0,007853982 8,56274395 0,00863299
0,22387211 9,44060876 0,013613568 9,46581474 0,01323755
0,25118864 10,4712855 0,018849556 10,4655031 0,01805527
0,28183829 11,6144861 0,023212879 11,5675636 0,02300346
0,31622777 12,7350308 0,027401669 12,7759207 0,02793755
0,35481339 14,1253754 0,031241394 14,1089305 0,03122337
0,39810717 15,4881662 0,03281219 15,4468009 0,03281915
0,44668359 16,9824365 0,040491639 16,9324399 0,03994675
0,50118723 18,6208714 0,039793507 18,6599488 0,04062058
0,56234133 20,4173794 0,040491639 20,4419864 0,04077219
0,63095734 22,1309471 0,040666172 22,2535082 0,04030093
0,70794578 23,9883292 0,039793507 24,0730245 0,03903895
0,79432823 26,0015956 0,036302848 25,8860102 0,03683531
0,89125094 27,8612117 0,033161256 27,6906702 0,03356849
1 29,5120923 0,029670597 29,5036296 0,02915535
1,12201845 31,2607937 0,023387412 31,3650408 0,02355591
porcentaje de error vout 0,32104739
porcentaje de error fase 3,06759033
Como se puede observar, los valores del porcentaje de error para ambas variables, el
voltaje de salida y la fase, se encuentran dentro de un rango considerado aceptable
para la precisión del modelo. Este resultado sugiere una adecuada concordancia
entre los datos originales y los generados, lo cual valida la capacidad predictiva
del modelo bajo las condiciones evaluadas. Existe la posibilidad de mejorar la
precisión incrementando el número de nodos en la capa de entrada y en las capas
ocultas, o bien, aumentando el número de capas ocultas. Sin embargo, esto también
incrementaría la complejidad del modelo.
Para mejor visualización de los resultados se convierten los resultados a sus
equivalentes en potencia de entrada, potencia de salida, y fase, y se comparan estos
datasheet para mejor visualización: