A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models.

Upon observation of RNN results, NN R[Y] outperformed NN R[0.5Y] in terms of performance.Although this advantage may not be significant, it is still meaningful as it suggests that longer memory leads to better predictive performance.While broader memory yields better results, it may not necessarily be the optimal choice due to its higher computational requirements and the need for increased processing power.

also in 1,181 other papers