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This methodology has been widely employed in many applications such as design optimization, response prediction and model validation.
Optimization based on fitness function cannot assure constant optimization response times which limits the genetic algorithm's use in real-time applications.
To improve the efficiency of optimization, response surface method (RS M is used to replace the time-consuming finite element simulations.
As a combination of mathematical and statistical techniques, response surface (RS) methodology has been successfully applied to design optimization, response prediction and model validation.
As a combination of mathematical and statistical techniques, response surface methodology has been successfully applied to design optimization, response prediction and model validation.
The design of experiment with response surface methodology and multiple optimization response was used to maximize the recovery of fluorescent probe at the end of the assay simultaneously with the optimization of target probe binding.
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In the fourth stage (multi objective optimization), responses were optimized simultaneously using grey relational analysis.
In the third stage (mono-objective optimization), responses were used alone in optimization study as an objective function.
A prototype DSPMLSM is made based on geometry optimization by response surface methodology (RS M considering multiple responses combined with 2D finite element analysis (FEA).
We divide these interactions into two classes, on the basis of their effects: the first is concerned with optimization of response levels (gain control), and the second is concerned with grouping of neural responses through enhanced neural co-activation (integration).
Important design parameters for a control system according to [14] include Filtration of input signals and optimization of response time delays Good tilt compensation ratios Designing control loops to minimise undesired car body behaviours ultimately optimising fast response.
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