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A D-optimal design was applied to setup statistically verified models for catalytic activity (XCH4,XCO2), selectivity (H2/CO molar ratio) and stability (coke resistance) as function of the selected input factors, viz. the amount of active element and amount of promoters.
For simplicity, the selected input membership functions are trapezoidal.
The results indicated that the selected input parameters and its interactions significantly influenced the MRR.
The selected input parameters were found to be sufficient for prediction of thermal properties.
The model was subjected to sensitivity analysis on selected input variables.
For simplicity, the selected input membership functions are trapezoidal, triangular or constant.
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In addition, MBFIPS ELM allows for the determination of the relative importance of the selected inputs.
Then the linear models were developed with selected inputs from the stepwise procedure.
Regression models were designed to predict the desired outputs as functions of selected inputs.
Yarn quality properties can be predicted by modelling selected inputs and outputs of the cotton spinning system.
A hybrid variable selection method is proposed for selecting input variables for support vector regression (SVR) model.
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