Modeling Methodology Based on Stacked Neural Networks Applied to the Photocatalytic Degradation of Triclopyr

In the present paper, we propose a modeling methodology based on stacked neural networks by combining several individual networks in parallel, whose outputs are weighted to provide the output of the stack. Also, a procedure was included for finding the optimal set of weights that leads to the best performance of modeling, on both training and validation data. As a case study, we consider the photocatalytic oxidation of triclopyr where the final concentration was evaluated depending on the reaction conditions, irradiation time and amounts of reactants. We show that the performance of the stack is better than those of individual networks, especially for the validation phase.