Optimized neural networks in industrial data analysis

The research work presented here tackles the problem of finding the optimum value for hyper-parameters, such as number of layers and number of neurons per layer, for a fully-connected Artificial Neural Network (ANN), particularly in regression problems. A proposed optimization strategy is tested on different datasets related to diverse industrial applications: (1) prediction of the performance of exploration algorithms for mobile robots, (2) prediction of the compressive strength of concrete, (3) prediction of energy output from a power plant; and (4) prediction of wine quality. Different evaluation metrics, such as Pearson correlation coefficient (R), Square Correlation Coefficient (R2), Absolute Relative Error (RAE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), are used to determine the best performing prediction model when the hyper-parameter optimization algorithms are used. That result is compared to the performance of the best model previously reported in the literature on the same dataset in order to determine the gains achieved by using the hyper-parameter optimization strategy under test.