Performance evaluation of ARX and ARMAX model based on PRBS and PRS perturbation

Abstract

This paper presents the comparison between ARX model and ARMAX model using the data from steam distillation essential oil extraction (SDEOE). The work is implementing system identification approach. The aim of this research is to identify the performance of ARX and ARMAX model toward the system. The input for every model is either Pseudo Random Binary Sequence (PRBS) or Pseudo Random Sequence (PRS) with temperature as output. Each dataset consists of 3000 sample, and being separated into estimation and validation for model estimation and validation with the ratio of 2000:1000, respectively. All the analysis work is done via Matlab R2013a. The result generally showed that ARX is slightly better than ARMAX model especially for model best fit. However, generally both models ARX and ARMAX can be implemented for PRBS and PRS perturbation since it passes all the validation's criteria in this study; model fits, correlation test and distribution of the residual. This finding is important and it will benefits for further work in steam distillation extraction especially for controller design and real time application.

Analysis on the Performance of a Second-order and a Third-order RLC Circuit of PRBS Generator

“…ACF of the input signal and CCF between input and output signal was used to estimate the transfer function model of the system [27]. In 2015, Ismail, et al compared ARX and ARMAX model performance using data from SDEOE, found that model fit for ARXs was between 95.77% and 95.06%, and the model fit for ARMAXs was between 95.68% and 95.05% [28].…”