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By using the RFE method, the prediction accuracies are further improved.
To verify the effectiveness of the presented method, the prediction experiment is carried out.
Trained by the results of FE simulations conducted by orthogonal experimental design method, the prediction system got a mathematical equation mapping the relationship between the process parameter values and warpage value of the plastic.
In the suggested method, the prediction horizon is variable and is adjusted in each step of the planning in agreement with the kinematic features of the closest obstacle in the visual field of the planner.
In this method, the prediction value of the current sample was the mean of its two closest samples.
In the sequence alignment method, the prediction was carried out by assigning the query peptide to the category of the peptide that has the highest sequence similarity with the query peptide.
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With the BMI method, the predictions range from being underestimated by 4.2 kg to an overestimation of 8.1 kg.
In the above-described correlation method, the predictions of the saliency models are compared to the fixation-distance maps of individual participants.
With the growth curve method, the predictions ranged from being under-estimated by 2 kg to an overestimation of 5 kg.
Taking back propagation (BP) neural network as a basic prediction method, the proposed prediction method is validated by output power prediction of a real wind farm.
Using numerical simulation methods, the prediction accuracy of the two ANN models and the performance of the algorithm were tested.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com