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The modeling performance was compare with traditional statistical analysis.
Support Vector Machine was used as multiple regression algorithm and modeling performance was evaluated by R2, RMSE and RPIQ.
The modeling performance was evaluated by the root mean square error (RMSE) and coefficient of determination (R2) as follows: R 2 = ∑ j o j 2 - ∑ j t j - o j 2 ∑ j o j 2, Open image in new window (4).
The modeling performance was validated by comparing the simulation with the corresponding observation, which included USDA forest biomass values, aboveground biomass from the National Biomass and Carbon Dataset 2000 [54], MODIS NPP, and the USDA grain yield for 2006, 2008, and 2010.
Modeling performance was evaluated by comparing the predicted and experimental temperatures at four locations: three within the pork sample and one for the high pressure fluid medium.
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The modeling performance is validated by comparing the model outcome with measured data extracted from a real device.
The implications of the MGFE method on the modeling performance are investigated through numerical simulations and frequency domain analysis.
We also examine whether the influence of component or object reuse on modeling performance is moderated by prior experience in systems analysis and design.
To further validate this result, the influences of the noise and quantization error of ADC on the modeling performance are analyzed.
The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE and Akaike Information Criterion AICIC).
The models, performances are evaluated based on two statistical indices to measure the modeling error and Taylor diagram.
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