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Models are optimized by multiobjective particle swarm optimization algorithm to achieve maximum specific energy absorption capacity and minimum peak crushing force.
The models are optimized to forecast the conditional variance of the petrol prices.
In present study, these regression models are optimized using principal component regression (PCR) method.
Moreover, all the models are optimized by B3LYP/6-31G B3LYP/6-31Gs set using the GAMESS program package [44].
The A-a to B-j models are optimized by using the DFT method at cam-B3LYP level of theory using the 6-31G (d) base set [38] when performing the GAMESS suite of programs [39].
In the first step, we selected 15 models for the adsorption of N2O gas on the exterior and interior surfaces of nanotube and then the considered models are optimized using the B3LYP/6-31G d B3LYP/6-31G dtheory.
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The hyperparameter values for all PCM models were optimized by 5-fold cross validation [38].
The parameters of both models were optimized and the effect of correlated variables studied.
The models were optimized by potential energy minimization using the SYBYL ® software package.
Both models were optimized by evolutionary computation, using a maximum likelihood estimator.
The developed regression models were optimized to maximize UTS of friction stir welded AA6061/AlNp composite joints.
Related(19)
predictions are optimized
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templates are optimized
matrices are optimized
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systems are optimized
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examples are optimized
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