Once the objective function is selected, most regression strategies consist of weighting the objective function by pre-selected values, usually based on experimental error estimates (i.e. standard deviation), convering the problem into a traditional weighted least squares minimization.
Their transfer function is selected to be the tan-sigmoid.
The shear stress parameter "m" that involves in shear strain function is selected through inverse method.
Master curves are obtained provided that an appropriate percolation function is selected.
A utility function is selected to reflect the objectives of the optimization problem.
Matrix coefficients and dominant poles of closed loop transfer function is selected based on Genetic Algorithm.
A priori probability function is selected so that smooth regions appear more probable than irregular ones.
The configuration that minimizes this cost function is selected as the specific Bayer CFA pattern.
The solution with the highest membership function is selected as the best compatible solution.
Brenner function is selected as the clarity evaluation function due to its excellent comprehensive performance.
The mean squared error performance function is selected to estimate the ANN performance.
