Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network

This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control signal, and outputs depicting temperatures inside the distillation column. One Step Ahead (OSA) model fitting and residual tests demonstrated that the RBFNN-based NARX model was able to approximate the system well, while satisfying all validation criterias.