Variable and delay selection using neural networks and mutual information for data-driven soft sensors

This paper proposes a new method for input variable and delay selection (IVDS) for Soft Sensors (SS) design. The IVDS algorithm is composed by the following steps: (1) Time delay selection; (2) Identification and exclusion of redundant variables; (3) Best variables subset selection. The IVDS algorithm proposed in this work performs the delay and variable selection through two distinct methods, mutual information (MI) is applied to delay selection and for variable selection a multilayer perceptron (MLP) based approach is performed. It is shown in the case studies that the application of the delay selection before applying the variable selection increases the generalization of the MLP-model. The algorithm uses the relative variance tracking precision (RV TP) criterion and the mean square error (MSE) to evaluate the precision of soft sensor. Simulation results are presented showing the effectiveness of the method.