Slurry erosion tests, carried out as per Taguchi's DOE protocol indicate that optimal laser parameters minimising the slurry erosion are laser power 100W, laser beam diameter 0.4mm and pulsing frequency of 10Hz.
Technically, this match is obtained by least-squares, adjusting the parameters to minimise the sum of the squared differences between ASM and VFM field strengths (see Olsen et al. 2003; Yin and Lühr 2011).
A genetic algorithm implemented in the "Genetic Algorithm and Direct Search Toolbox" of Matlab (the ga command) was then used to find the parameters which minimised the error function.
A genetic algorithm (Gulsen et al. 1995; Vander Noot and Abrahams 1998; Kapanoglu et al. 2007) is used to search the model parameters by minimising the weighted quadratic error between simulated data and target microdialyses reported in Fig. 1a c (DA and NE dynamics in vmPFC, and DA dynamic in the NAcc, in the three different conditions reported in Pascucci et al. 2007).
Cutting fluid pressure, feed rate and spindle speed were the input parameters whilst minimising surface roughness on the crest of the thread was the target.
In this work, the tubular shape fitting algorithm involves finding the tube parameters that minimise the point to surface distance to the region M. The algorithm creates a tube around the curve c p (see previous section) and optimises its radius to tighten the shape around the main stem (see Figure 4.a).
The RLS algorithm with FF approaches the problem of estimation of non-stationary (time-varying) signal model parameters by minimising the sum of exponentially weighted squared residuals [5 7, 25, 26].
Using this parameterisation of the anomaly, we proceed as before and adjust the set of parameters to minimise the differences between the ASM readings and the strength of the magnetic field observed by the VFM instruments through a least-squares fit.
As the simulation model produced stochastic time series and random spatially discrete distributions of individuals we estimated parameters by minimising the expectation of weighted sum of squares.
Rigorous and uniform analysis of cardiac I-MIBG parameters minimises inter-and intra-individual variation [ 15, 23].
