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The performance of each function is shown by means of some examples with real data sets.
Using examples with real data, Koretzky will address the importance of both Feature Engineering and Feature Learning and how they can work together.
Examples with real wind climate model and generic wind tunnel test results are shown to illustrate the influences brought by directional dependence, model difference, and wind speed masking.
Examples with real data show the effectiveness of our proposed techniques by demonstrating that using BNNs can reduce load forecasting errors, compared to various existing techniques.
The advantages and limitations of various codon-based models are compared, and illustrated by examples with real biological complexities such as slow codons, premature termination and feedback regulation.
The latter finding provided the rationale for taking C0 = 4.0 when constructing confidence intervals for mortality risks in our examples with real data.
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Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches.
An example with real world data illustrates the plausibility of the findings.
Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered weighted averaging operator than the weighted averaging operator in defending against the strategic weight manipulation of the MADM problems.
In Section 4 we consider an example with real data.
A practical example with real areas can be addressed for the Message RAMs for 360 and 45 FUs, to give a better perspective of the problem.
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