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ELM is a single-hidden layer feedforward network (SLFN) which randomly selects input weights and hidden neuron biases without training.
The multiplexer either selects input data from the image for the first-level decomposition or from RAM for higher-level decompositions.
First, the user selects input criteria and second, output options from an extensive list of possibilities provided.
The current study selects input model parameters from different studies as no single source could be identified for all of the necessary data.
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A hybrid variable selection method is proposed for selecting input variables for support vector regression (SVR) model.
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.
The list of selected input and output indicators is presented in Table 2.
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