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In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive (AR) model coefficients are generally considered an efficient and robust feature set.
(4) The AR model is constructed and model coefficients are saved for user verification.
The principal h-d models also vary on how the model coefficients are being interpreted, which is especially important if they are then modelled as smooth functions of predictors.
The MNL model coefficients are difficult to interpret.
The related model coefficients are determined with measurable parameters.
Model coefficients are developed using ordinary least squares (OLS) regression.
The model coefficients are identified in terms of measurable parameters.
The physical implications of the model coefficients are also discussed.
Model coefficients are calibrated for each month using historical data.
The model coefficients are then recomputed using weighted least squares (WLS).
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The proposed approach is validated in example applications, where the Sobol' indices derived from the meta-model coefficients are compared to reference indices, the latter obtained by exact analytical solutions or Monte-Carlo simulation with extremely large samples.
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