This work approximates the selective harmonic elimination problem using Artificial Neural Networks (ANN) to generate the switching angles in an 11-level full bridge cascade inverter powered by five varying DC input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195W solar panel. The angles were chosen such that the fundamental was kept constant and the low order harmonics were minimized or eliminated. A non-deterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles\u27 precision and is able to perform in real time (50/60Hz time window)
