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Exact(5)
Modelling errors can occur when using PPC data for parametrisation since the model is optimised over a narrower bandwidth than the validation profile.
Optimal latencies are 16.90 (linear), 13.60 (transient), and 13.60 if the two-phase model is optimised with positive ERR0.025.
The model is optimised over an ensemble of ERG waveforms from two energy levels, 1.22 and 1.52 log cd·s·m−2, circles : data, lines : model).
The optimal latencies are 38.58 (linear), 36.90 (transient), and 36.90 if the two-phase model is optimised with ERR0.025,φ ≥ 0. At φm = 36.90 the two-phase model has LRT2p-con = 34.874 while LRT2p-trans = 16.755 and LRT2p-lin = 27.642, all highly significant comparisons.
Instead, a model is constructed for the data and the likelihood of the model is optimised, with no need to explicitly consider distances between clusters.
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The parameters for each model were optimised to give the lowest mean squared error.
The coefficients of the semi-active model are optimised to maximise the harvested power.
The performance of the model was optimised by calibrating different parameter values.
The model was optimised to match both the HPMC tablet radius and the concentration profiles over time.
Whilst a 40-layer model was optimised in the work of Carter et al.[32], this made use of a mixed atom pseudo-potential and is not explicitly comparable to the models presented here.
In this work the influence of R on the fatigue crack propagation resistance in a PM duplex stainless steel was investigated and an artificial neural network based model was optimised as a new simulation tool.
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