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The advantages of ANNs over conventional mathematical methods in modeling performance have been recognized and confirmed by many research reports.
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Model performance has been evaluated by comparing the global modeling outcomes of numerous aerosol modules.
The modeling study employed different input combinations, and model performances have been estimated by means of several indicators.
This paper presents an approach to modeling workers where human performance has a significant impact on system productivity.
This condition shows that the desired performance has to be traded against modeling accuracy.
Performance has been mixed.
Performance has recovered somewhat.
Performance has fallen back".
Fund performance has improved.
In addition, the correlation of parameters (clearance, volume distribution, etc) has significant effect on modeling performance, and it is checked by scatter plots or a scatterplot matrix.
Based on AME and MAE, the seasonal and global models had the smallest error in their modeling performance.
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