In this work, the ANN was trained using the following procedure: the complete dataset was randomly partitioned into training, validation, and test subsets, with proportions of 80%, 10%, and 10%, respectively. The validation data served two critical roles: first, it provided a check against overfitting, a phenomenon where a model learns the details and noise in the training data to an extent that it negatively impacts the performance of the model on new data (for example, by memorizing the training data rather than learning generalizable patterns). Second, it evaluated the model's ability to generalize, meaning how well it performs on new, unseen data-an important indicator of its practical applicability. The test subset was then used to objectively evaluate the performance of the ANN. A comparative analysis of training algorithms showed that the LM-based approach was the most effective for the datasets under consideration. To achieve an ANN with desired generalization abilities (minimized validation error), an early stopping strategy was integrated into the training routine, halting the process when no further reduction in validation loss was observed.