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By converting the dynamics of switched BCNs into an algebraic form and using the redundant variable separation technique, all possible state feedback controllers are designed for the disturbance-independent output decoupling problem.
This model was refined by stepwise removal of the redundant variable of differentiation (Table 2, b).
Stepwise regression chose a model with two variables - the true predictor together with a redundant variable.
The estimated probability of not selecting a redundant variable was between 0.99 and 1 for Bayesian model averaging while approximately 0.95 for stepwise regression when the redundant variable was not correlated with a true predictor.
We found that Bayesian model averaging almost never selected a redundant variable, whereas stepwise regression did - 1 time out of 20 for a redundant variable not correlated with the true predictor.
In this case the probability of selecting a redundant variable was dependent on the effect size of the true predictor.
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By discarding useless or redundant variables, not only it improves model performance but also facilitates its interpretability.
In this study, we develop an ad hoc iterative feature (variable) reduction algorithm for basic PNNs to identify noise and redundant variables.
Although the support vector machine (SVM) can be applied to forecast solar radiation, it includes a large number of redundant variables.
Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables.
Under the framework of output-friendly subspace, we give a necessary and sufficient condition for the solvability of DDP by analyzing the redundant variables, and we present a computationally feasible method to construct all the valid feedback control matrices.
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