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A multiple response function was established in order to get an experimental condition for simultaneous extraction of copper and zinc.
The optimization step was performed using two-level full factorial and Box Behnken designs, being that a new multiple response function was established.
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Furthermore, a unified partitioning strategy is proposed to model multiple response functions defined on the same domain.
The mathematical model of ANN-MRSM is established by using artificial neural network to fit the multiple response surface function.
The use of characterized excitation and choice of averaging techniques are fundamental to the estimation of multiple input, multiple output (MIMO) frequency response function (FRF) data.
During multiple response optimization, the desirability function approach was employed to concurrently maximize Y1 and minimize Y2.
Response surface methodology and artificial neural networks, followed by multiple response optimization using desirability function approach, were applied to individually and simultaneously evaluate fatty acid methyl ester (FAME) yield and exergy efficiency in process of the in-situ transesterification.
In this paper we investigate a multiple response problem within desirability function framework and try to determine values of input variables that achieve a target value for each response through three meta-heuristic algorithms such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS).
Multiple response optimization applying desirability functions in RSM and the use of artificial neural networks for modeling are also discussed.
Multivariate designs combined with the concepts of multiple response treatments and desirability functions have been employed to simultaneously optimize and evaluate the responses.
Statistical parametric maps were calculated using multiple regression with the hemodynamic response function modeled in SPM5.
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