System identification for essential oil extraction system: An overview

Rahiman, Mohd Hezri Fazalul; Taib, Mohd Nasir; Salleh, Y.M.

doi:10.1109/cspa.2009.5069252

Cited by 5 publications

(3 citation statements)

References 74 publications

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“…Pre-processing is an important step for data conditioning prior to identification. In experiments conducted in this work, the division for the Flexible Robot Arm (FRA) dataset was 50% for training set and 50% for testing set based on the recommendation by [51].…”

Section: B Methodologymentioning

confidence: 99%

Binary Particle Swarm Optimization Structure Selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model of a Flexible Robot Arm

Yassin¹,

Zabidi²,

Ali³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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The Nonlinear Auto-Regressive Moving Average with Exogenous Inputs (NARMAX) model is a powerful, efficient and unified representation of a variety of nonlinear models. The model's construction involves structure selection and parameter estimation, which can be simultaneously performed using the established Orthogonal Least Squares (OLS) algorithm. However, several criticisms have been directed towards OLS for its tendency to select excessive or sub-optimal terms leading to nonparsimonious models. This paper proposes the application of the Binary Particle Swarm Optimization (BPSO) algorithm for structure selection of NARMAX models. The selection process searches for the optimal structure using binary bits to accept or reject the terms to form the reduced regressor matrix. Construction of the model is done by first estimating the NARX model, then continues with the estimation of the MA model based on the residuals produced by NARX. One Step Ahead (OSA) prediction, Mean Squared Error (MSE) and residual histogram analysis were performed to validate the model. The proposed optimization algorithm was tested on the Flexible Robot Arm (FRA) dataset. Results show the success of BPSO structure selection for NARMAX when applied to the FRA dataset. The final NARMAX model combines the NARX and MA models to produce a model with improved predictive ability compared to the NARX model.

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Section: B Methodologymentioning

confidence: 99%

Binary Particle Swarm Optimization Structure Selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model of a Flexible Robot Arm

Yassin¹,

Zabidi²,

Ali³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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“…The NAR model can be constructed using various methods [46][47][48][49][50][51][52][53][54][55], although the polynomial approach is the only method that can explicitly define the relationship between the input/output data.…”

Section: Nonlinear Autoregressive Model (Nar)mentioning

confidence: 99%

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Zabidi¹,

Yassin²,

Tahir³

et al. 2018

J. Fundam and Appl Sci.

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This paper presents a comparison between the Binary Artificial Bee Colony (BABC) and Binary Particle Swarm Optimization (BPSO) algorithm for structure selection of a Nonlinear Auto-Regressive Model (NAR) of the chaotic Mackey optimization algorithms are swarm BPSO mimicking the swarming behavior of birds. Recent research has suggested that the ABC algorithm has better solution quality compared to PSO. However, resea this advantage applies to the structure selection case in system identification has not been investigated. We conduct extensive tests to determine the convergence performance of both algorithms.Author Correspondence, e-mail: . Zabidi et al. J Fundam Appl Sci. 2017, 9(3S), 730-754 731 It found that the BABC had managed to significantly outperform BPSO in terms of convergence consistency with a slight advantage in terms of solution quality.

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“…The essential oil will evaporated and undergo the condensation process. Essential oils do not fuse well with water so it frames a thin film on the water surface [4]. The essential oil is collected, dried with anhydrous sodium sulphate and stored at 4 • C until used [5].…”

Section: Introductionmentioning

confidence: 99%

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

Ismail

Rani

Rahiman

et al. 2015

2015 IEEE Conference on Systems, Process and Control (ICSPC)

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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.

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System identification for essential oil extraction system: An overview

Cited by 5 publications

References 74 publications

Binary Particle Swarm Optimization Structure Selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model of a Flexible Robot Arm

Binary Particle Swarm Optimization Structure Selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) Model of a Flexible Robot Arm

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

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

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