| Summary: | Optimized parameters of dual-pump fiber optic parametric amplifier (FOPA) to give optimized
FOPA gain can be obtained through optimization techniques. However, it is complicated to
determine the multi-objective functions (gain, bandwidth and flatness), multi decision variables
and multiple global solutions. Optimization works only considered undepleted pump configura�tion or pump depletion but without fiber loss. Recently, a machine learning approach was applied
to design a Raman amplifier. Thus, this study intends to design a desired dual-pump FOPA gain
utilizing an artificial neural network (ANN) to predict pump powers and pump wavelength by
considering pump depletion and fiber loss. First of all, the FOPA training gain data were obtained
through the 6-wave model and supplied into the ANN to learn the relation between the gains with
their pump wavelengths and pump powers. Once the smallest mean square error (MSE) between
input and target was obtained, the ANN model was saved. The ANN model can be used to predict
the desired pump wavelengths and pump powers if the desired gain is given. The desired gains of
constant values from 10 to 45 dB over 1540–1589 nm for optical communication are predicted
very well with mean absolute error (MAE) of 1 dB variations.
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