MANUFACTURING PROCESSES MODELING USING MULTIVARIATE INFORMATION CRITERIA FOR RADIAL BASIS FUNCTION SELECTION - Praga-Alejo, Rolando Javier; de León-Delgado, Homero; González-González, David Salvador; Cantú-Sifuentes, Mario; Tahaei, Ali (International Journal of Industrial Engineering: Theory, Applications and Practice, 2022)
Nowadays, the advances in manufacturing technologies need to improve processes every day. For this reason, it is useful to have models for planning, optimization, simulation, and decision-making in the process. A widely used method to improve manufacturing processes is the Radial Basis Function Neural Network, which models the manufacturing process using a nonlinear radial function. There are several types of radial basis functions, but the question is: which specific function generates a better representation? This work proposes the application of Information Criteria based on Akaike’s criteria to select the radial basis function that best describes the process behavior. The present paper proposes the design of an RBF Network to predict the behavior in processes with several responses, applying the multivariate Akaike Information Criteria (AIC) as a fitness function in a Genetic Algorithm to select the Radial Basis Function to improve the prediction of the Radial Basis Function neural network.