Abstract
Induction machines are widely preferred in plants due to their uncomplicated structure and low maintenance requirements. In order to achieve effective control over the operations of these machines, it is crucial to possess accurate information about their parameters. The estimation of these parameters can be accomplished through the utilization of artificial neural networks. Nevertheless, the majority of studies undertaken for parameter estimation were inadequate in accurately representing the network architecture's performance or achieving the desired precision. This was mostly due to the low amount of available data and the reliance on data from a single experimental setting. This study evaluates a recurrent neural network with a concise and flexible structure to address data insufficiency and the reliance on a singular experimental setting. This evaluation involves using a substantial dataset and optimizing the network parameters to achieve the most efficient network structure. Upon completion of the study, the proposed approach demonstrated promising results with high correlation levels and minimal error rates.