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By applying these new techniques, the models complexity and computational cost is significantly decreased.
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This information usually pertains to model complexity and is used to penalise excessively complex models.
However, as the model complexity and number of parameters increase to better approximate the tissue's complex behavior, estimating model parameters uniquely and accurately becomes an increasingly challenging task (Xi et al. 2011b).
There is a trade-off between within class model complexity and number of classes where more classes can compensate for a less complex within class structure [ 25, 26].
This implies a tradeoff between model complexity and model selection.
Model complexity and computation burden would increase rapidly with a longer prediction horizon.
The parameters are used to control the model complexity and training error.
The abstracted model aims to reduce model complexity, and yet maintain validity.
However, solution quality, model complexity, and searching time have not been considered thoroughly.
This leads to further reduction in model complexity and in number of parameters.
The influence of the model complexity and model parameters uncertainly on the quality of safety analysis would be underline.
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