Una vez definida la arquitectura, habitualmente de 3 o 4 capas, con todas las neuronas entre capas adyacentes conectadas entre sí, y conocida la función de activación, únicamente queda determinar la regla de aprendizaje. En las redes multicapa se utiliza el gradiente del error para ajustar los pesos, el cual bien dado por la expresión de la Ecuación 5:
Siendo:
Xk(t): entrada ponderada a la neurona k (calculada según la Ecuación 2) en la iteración t.
yk(t): salida real de la neurona k en la iteración t (calculada según la función de activación de la Ecuación 4).
: salida esperada en la neurona k.
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