Información del artículo
Resumen
En este trabajo se aborda, de manera particular, un método para el diseño del algoritmo conocido como Group Method of Data Handling, GMDH, típico con lazo recurrente. Una modificación en una de sus fases de entrenamiento permite ampliar el número de variables utilizadas en cada capa y con ello el área de regresión. Consecuentemente se puede obtener una estructura optimizada en sí misma de mayor complejidad, posibilitando la aparición de lazos recurrentes en las capas intermedias. Lo anterior permite una reducción del error en la modelación de procesos no lineales de lento comportamiento, como el crecimiento celular en biorreactores. El modelo se probó en una fermentación tipo feed-batch de la levadura Pichia pastoris. La estabilidad y capacidad de generalización es demostrada. El método propuesto es comparado con el GMDH típico recurrente y con otras estructuras de redes neuronales clásicas.
Palabras clave:
redes neuronales
recurrente
algoritmo genético
modelación
fermentación
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Referencias no citadas
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