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Back-propagation artificial neural network and response surface methodology were used to investigate estimation capabilities of these two methodology and optimization acceptability of desirability functions methodology in an air drying process.
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These two methodologies are based on different assumptions concerning their application.
In the following, a brief description of these two methodologies is given.
The coupling of these two methodologies is realized by a time-splitting method.
These two methodologies have similarities and differences; in this paper we clarify their roles.
In light of this supposition, they exhibited how these two methodologies are utilized to recognize what's more, attribute maliciousness to the various threat.
Our approach combines the HDG methodology with geometrical optics in a fashion that allows us to take advantage of the strengths of these two methodologies.
A compromise between these two methodologies is represented by the use of data assimilation scheme to include sparse remote estimates of surface fluxes into continuous modelling.
These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort.
Although the integration of these two methodologies has now started, in the future synergistic integration should enable many new opportunities for bioprocesses for the production of chemicals.
Although prior studies have examined the relationships between these two methodologies through similar experiments, they have produced conflicting results in the literature for a variety of reasons.
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