NDLI: Selection of training data for modeling essential oil extraction system using NNARX structure

Content Provider	IEEE Xplore Digital Library
Author	Rahiman, M.H.F. Taib, M.N. Salleh, Y.M.
Copyright Year	2007
Description	Author affiliation: Univ. Teknologi MARA, Shah Alam (Rahiman, M.H.F.; Taib, M.N.; Salleh, Y.M.)
Abstract	In this work, the suitable training data for modeling the steam distillation essential oil extraction system is presented. The data were collected from the self-refilling distillation column using RTD sensor with associated signal conditioning circuit. The control signal is on/off The heating system implementing 1.5 kW electrical immersion heater. The power switching is performed using zero-crossing solid-state-relay. The input signals are the PRBS with different probabilities. There are 3 situations of data to be investigated. Since the system is highly-nonlinear, it is expected that the training data that covers the full operating condition will be the optimum training data. These data are separated into training and testing data by interlacing technique, which make the total number of data 6. For each data, the model order selection is based on ARX structure and MDL information criterion. These data are cross-validated between each others and the validation results are presented and concluded. The performance indexes are the percentages of $R^{2}$ , $adjusted-R^{2}$ and NMSE.
Starting Page	1691
Ending Page	1695
File Size	781911
Page Count	5
File Format	PDF
ISBN	9788995003862
DOI	10.1109/ICCAS.2007.4406607
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-17
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	ICASE
Subject Keyword	NNARX model Distillation equipment Circuits Training data Control systems Temperature control Data mining Power system modeling training data for global model Petroleum Resistance heating Testing
Content Type	Text
Resource Type	Article

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Black Box Modeling of Steam Distillation Essential Oil Extraction System Using NNARX Structure

Performance of multi-step-ahead-prediction ARX for steam temperature in a self-refilling distillation essential oil extraction system

Assessment of NNARX structure as a global model for self-refilling steam distillation essential oil extraction system

Model Predictive Control using ARX model for steam distillation essential oil extraction system

Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process

Noise variation test for ARX model of a heating process in essential oil extraction plant

Self tuning PID controller for steam distillation essential oil extraction system

Black box modeling of steam distillation essential oil extraction system using ARMAX structure

Analysis of weight decay regularisation in NNARX nonlinear identification

Selection of training data for modeling essential oil extraction system using NNARX structure

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Black Box Modeling of Steam Distillation Essential Oil Extraction System Using NNARX Structure

Performance of multi-step-ahead-prediction ARX for steam temperature in a self-refilling distillation essential oil extraction system

Assessment of NNARX structure as a global model for self-refilling steam distillation essential oil extraction system

Model Predictive Control using ARX model for steam distillation essential oil extraction system

Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process

Noise variation test for ARX model of a heating process in essential oil extraction plant

Self tuning PID controller for steam distillation essential oil extraction system

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Analysis of weight decay regularisation in NNARX nonlinear identification

Selection of training data for modeling essential oil extraction system using NNARX structure