Bootstrapping neural network regression model for motor drive vibration optimization through Genetic Algorithm

This work proposes an optimization procedure based on a bootstrapped neural network interpolation approach and the Genetic Algorithm method. The bootstrapped neural network is used to generate designed data sets in order to estimate a mapping from input to output space in an intrinsic experiment in a motor drive vibration study. The optimization procedure is aimed to minimize the motor vibration by adjusting some drive control parameters.