Sentence examples for model which minimises from inspiring English sources

Manzini et al. (2015) presented a nonlinear model which minimises cost of preventive and corrective maintenance as well as technicians' workload and spare parts cost.

By assigning a cost to each of the four types of events, we can obtain a parsimonious solution (or simply a reconciliation) according to the DTL model which minimises the total cost of the mapping.

One approach to selecting the best fitting curve is to chose the model which minimises Akaike's Information Criteria, given as - 2 log L ^ + α q [ 5], where q is the number of unknown parameters and α is a constant, generally taken as 2. The mean and standard deviation for each parameter, the covariance between parameters, and the Cholesky matrix, C, can be recorded from the output.

The standard non-regularised least squares problem (Aster et al. 2005; Gubbins 2004) involves solving an optimisation problem to find the model m which minimises the objective function Thetaleft(mathbf{m}right) = left[mathbf{d} - mathbf{A}mathbf{m}right]^{T} mathbf{C}_{e}^{-1/2}mathbf{W}_{k}mathbf{C}_{e}^{-1/2}left[mathbf{d} - mathbf{A}mathbf{m}right], (4).

We seek to find the model ((mathbf {m})) which minimises the model roughness ((R(mathbf {m}))) subject to the constraint that the model misfit ((chi ^2(mathbf {m}))) is less than a desired target misfit ((chi ^2_mathrm{target})).

The simulated 3D mass distribution has been derived through an interactive and iterative mass model optimisation algorithm, which minimises geoid height differences with respect to a degree-360 spherical harmonic expansion of the EGM2008 global external gravity field model.

Let Fit(c) be the value of the sum of the squared distance for each position of the centromere for one interference model; the best theoretical centromere position under this model is deduced by searching c, which minimises Fit c).

The model objective is to find the schedule for each ship which minimises total operating and penalty cost.

The reference model is designed to be invariant in the long term, which minimises the need for schema and software updates [ 32].

We solved the following optimisation problem which minimises the 2-norm of the error between model prediction and observation (least squares minimisation): (23) where p corresponds to the number of measurements.

Group comparisons were performed by fitting the data at each brain voxel (in which all subjects had non-zero data) using a linear model, which was fitted by minimising the sum of absolute deviations, rather than the sum of squares, in order to reduce outlier effects.