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An alternative to the use of desirability function for optimising multiple responses is proposed.
In addition, it was found out that the percentage improvement in GRG with the multiple responses is 39.4%.
In the probabilistic context, validation metric for models with multiple responses is essentially used to measure the difference between joint statistical distributions resulting from simulation predictions and experimental observations respectively.
To simultaneously optimize the parameter robust design of dynamic multiple responses is difficult due to product complexity; however, the design is what determines most of the production time, cost, and quality.
One simple method to deal with multiple responses is to fit binary logistic fitting for each pair of these multiple categories.
In the case of the Big Data situation arising in biological information processing, the pivotal operation of resolution of multiple responses is performed by a certain stream algorithm [ 29].
Similar(54)
Multiple responses were simultaneously analyzed by optimization.
The dependent parameters as multiple responses were evaluated.
In Statistics we normally use weighted frequencies for each person when multiple responses are available.
However, for many applications joint predictions of the multiple responses are needed.
The multiple responses are optimized simultaneously using Taguchi based grey relational analysis.
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