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Optimising the model specifically for fire risk purposes would undoubtedly increase its precision.
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This method aims at optimising the modelling to the specific target being used, while integrating, into the modelling, a variety of biological signals.
To adjust the models to the constraints imposed by the electrophysiological data, we optimised the model parameters by means of an optimisation method.
Subsequently, PSO was used to optimise the model based on mentality constraints.
Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena.
However, not all research has optimised the model's guidelines, with a distinct failure to account for personal relevance when designing imagery scripts or selecting tasks for use in studies.
Next, it employs continuously updated electrical data in order to optimise the model's predictive ability, with zero-order hold elements enforced on flowrate and composition data until these variables are resampled.
The second part optimises the model in respect to the given prior knowledge.
Further research including internal and external validation studies must be performed to optimise the model before clinical implementation.
The first part of this error term is the same as in equation 2 and is used to optimise the model in respect to the measured time series data.
We began this study to optimise the model by comparing two different protocols: 200 µl of serum at day 0 (as in 22) or 50+50 µl at day 0 and day 2. A remarkably similar arthritic response could be measured with respect to disease profile and intensity (figure 1A).
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