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The optimization model is presented with the distortion as the optimization objective, and the model is optimized with an upper limit, a lower limit and the constraint function by the non-linear method and the Lagrange multiplier method.
The model is optimized with Rosenbrock's method.
Thus, the model is optimized automatically for a certain machining process and/or dynamic behavior.
The lay-up of the model is optimized to attain a final lay-up with the minimum weight.
The model is optimized using an approach based on artificial immune systems, and the results of the computational experiments are reported.
Efficient means that model generation is possible for data with more variables than objects, for data with highly correlating variables, and that the complexity of the model is optimized for best prediction performance (not necessarily for best fit).
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The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h.
For prediction, the model was optimized using an iterative procedure rather than simultaneous optimization of all parameters using Baum-Welch.
The model was optimized by response of sensory evaluation score and L*.
The model was optimized to determine the feed and shell flow rates which maximize the yield.
The model was optimized for the maximum biosurfactant production by using genetic algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com