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The desirability function was used to optimize the response variables, each having a different target and the observed responses were highly agreed with experimental values.
The desirability function was used to optimize the response variables, each having a different target, and the observed responses were in accordance with the experimental values.
The desired function was used to optimize the response variables, each with a different target, and the observed responses were in good agreement with the experimental values.
Response surface methodology (RS M, particularly Box Behnken design (BBD) was used as modelling tool, and obtained predictive function was used to optimize the overall process by the means of desirability function approach (DFA).
The desirability function was used to optimize the response variables.
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Three optimization methods with different objective functions are used to optimize the layout.
A least square cost function was considered and gradient descent was used to optimize the parameters [ 1].
The nls function implemented in R-language (Ihaka and Gentleman, 1996) was used to optimize the parameters.
A continuous minimization method by simulated annealing was used to optimize the equilibrium frequency vector π, with the objective function being the logarithm likelihood of the alignment.
Cross-validation was used to optimize the parameters M, K and D of the classification function for identification of novel classes of qnr genes.
Manual data fitting was used to optimize the interactions in the model and the shapes of the functions relating input and output species in the fuzzy logic gates.
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