Modeling Induction-based Steam Distillation System by Using Nonlinear Auto-Regressive with Exogenous Input (NLARX) Structure

This paper presents the performance of Non-Linear Auto Regressive with Exogenous input (NLARX) model structure that is applied in modeling of induction based steam distillation system. The input is Pseudo-Random Binary Sequence (PRBS) and the output is temperature. The input-output data was split into two equal set for model estimation and model validation. All the data are transferred to MATLAB R2013a software for analysis. Wavelet Network, Sigmoid Network, Tree partition Network and Feedforward Neural Network are the nonlinearity estimators used to build the NLARX model structure and their performances have been compared. The validation of estimated model will be based on best fit (R-2), final prediction error (FPE), loss function, auto-correlation function (ACF) and cross correlation function (CCF). The result showed that NLARX with Feedforward neural network is the most suitable estimator among others due to it yields the highest percent of best fit (R-2), lowest final prediction and loss function, and all the coefficients are within the confidence limit for CCF test.