  Artificial neural networks make it possible to identify black-box models.
Based on a recurrent nonlinear autoregressive exogenous neural network, this
research provides a technique for simulating the static and dynamic behavior of
a DC-DC power converter. This approach employs an algorithm for training a
neural network using the inputs and outputs (currents and voltages) of a Buck
converter. The technique is validated using simulated data of a realistic
Simulink-programmed nonsynchronous Buck converter model and experimental
findings. The correctness of the technique is determined by comparing the
predicted outputs of the neural network to the actual outputs of the system,
thereby confirming the suggested strategy. Simulation findings demonstrate the
practicability and precision of the proposed black-box method.
