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

Yassin, Ihsan Mohd; Zabidi, Azlee; Ali, Megat Syahirul Amin Megat; Tahir, Nooritawati Md; Abidin, Husna Zainol; Rizman, Zairi Ismael

doi:10.18517/ijaseit.6.5.919

Cited by 45 publications

(42 citation statements)

References 62 publications

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“…This was done using MATLAB command line using iddata function. The OSA prediction is representing as Equation [10]:…”

Section: Model Estimationmentioning

confidence: 99%

“…2017, 9(4S), 145-159 147 important tool for many sector in order to develop the sophisticated algorithms. Academically, system identifications mean that the process to build the mathematical model from the set of input output data to describe the underlying process [9][10][11]. Usually, modelling using system identification approach is necessary to study the dynamic behaviour about the extraction process.…”

Section: Introductionmentioning

confidence: 99%

“…In a series of existing papers stated that modelling is necessary for controller design, predict and study the dynamic of a process, to prevent any dangerous situation, predict the energy consumption and optimize the operation condition. Modelling can be divided into two Network Auto-regressive with Exogenous Input (NNARX) and NARMAX [10,24].…”

Section: Introductionmentioning

confidence: 99%

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The study of dynamic response using ARX model in extraction process

Yusoff¹,

Aziz²,

Razali³

et al. 2018

J. Fundam and Appl Sci.

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This work presents a model using system identification approach namely as ARX to represent the dynamic response for essential oil extraction process. A disturbance was collected using MATLAB Simulink. The 3000 samples of data was collected by using PRBS as an input and temperature in separated into two groups; training data and estimation dThe model estimation was done by using linear regression method. The robustness of the model was evaluated by using best fit (R analysis and residual analysis (histogram successfully capturing the dynamic response of extraction process by provide the high best fit, low RMSE error and normally distributed by producing small mean and variance. This work presents a model using system identification approach namely as ARX to represent the dynamic response for essential oil extraction process. A fresh set of data under feed in disturbance was collected using MATLAB Simulink. The 3000 samples of data was collected by using PRBS as an input and temperature in o C as an output. The collected data was separated into two groups; training data and estimation data by using interlacing technique.The model estimation was done by using linear regression method. The robustness of the model was evaluated by using best fit (R 2 ), OSA, root mean square error (RMSE), correlation analysis and residual analysis (histogram). Based on validation results, the ARX model was successfully capturing the dynamic response of extraction process by provide the high best fit, low RMSE error and normally distributed by producing small mean and variance.regressive with exogenous input (ARX); pseudo-random binary (PRBS);hnique; prediction error method; one-step ahead prediction (OSA). This work presents a model using system identification approach namely as ARX to represent set of data under feed in disturbance was collected using MATLAB Simulink. The 3000 samples of data was collected C as an output. The collected data was ata by using interlacing technique.The model estimation was done by using linear regression method. The robustness of the ), OSA, root mean square error (RMSE), correlation ). Based on validation results, the ARX model was successfully capturing the dynamic response of extraction process by provide the high best fit, low RMSE error and normally distributed by producing small mean and variance.random binary (PRBS); step ahead prediction (OSA).

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“…This was done using MATLAB command line using iddata function. The OSA prediction is representing as Equation [10]:…”

Section: Model Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The study of dynamic response using ARX model in extraction process

Yusoff¹,

Aziz²,

Razali³

et al. 2018

J. Fundam and Appl Sci.

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show abstract

“…The load flow of the system has been simulated to get the desired parameters for further optimization. For optimization process, Particle Swarm Optimization (PSO) [15] has been introduced and implemented in the distribution system model. There are several cases that have been considered for this study.…”

Section: Case Studymentioning

confidence: 99%

Optimal capacitor sizing and placement based on real time analysis using PSO for energy efficiency improvement

Rahman

Latip

Zaini

2018

J. Fundam and Appl Sci.

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“…Not only better precision but also skilful in the model fit enhancement and correlation violations (CRV) number decline were reported in the proposed BPSO method [22][23][24]. Additionally, for structure selection M. H. F. Rahiman et al J Fundam Appl Sci.…”

Section: Introductionmentioning

confidence: 97%