On the other hand, AE is a popular unsupervised feature learning model, which aims to make the encoded outputs to be equal to the original inputs by minimizing the reconstruction errors [31].
Model predictive control finds an optimal set of control inputs by minimizing an objective function while preventing violations of predefined constraints.
Patients were considered as producers, seeking to avoid waste by obtaining maximised outputs from given inputs or, by minimizing input use in the production of given outputs [ 22]; where, in this context, "maximize" is used in the sense of optimize.
The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem.
To integrate the two control algorithms, the control input is determined by minimizing the weighted sum of the predicted error from the deterministic model and the information entropy of the error probability density distribution.
This algorithm has the ability to learn itself and to use the input data directly, by minimizing the sum of squared errors, in order to obtain the parameters, namely the number of neurons and the type of kernel functions needed for the fitting process.
For a given feedback gain which asymptotically stabilizes the closed-loop system, a sequence of free control inputs is calculated by minimizing the nominal performance cost over the perturbation horizon N. The nominal model of the process is included in the controller design in order to improve control performance.
This refers to a computational framework for inferring the causes (intentions, goals, and motor commands) of sensory inputs (observed kinematics) by minimizing prediction error at all levels of a cortical hierarchy.
The four conformations intermediate between the input and the local minimum are obtained by minimizing the input employing an additional flat bottom potential with flat bottom radius of 0.2, 0.6, 1.0 or 1.4 Å to constrain their deviation from the input conformation.
The delay and dispersion of the arterial input function was corrected by minimizing the residual sum-of-squared errors of model fitting to the whole-brain time activity curve.
