This control design framework offers, in addition to constraint satisfaction, also the possibility of including an optimization objective that can be used to minimize steady state (tracking) errors, to decrease the settling time, to reduce overshoot and so on.
However, after the first epoch, while training error continues to decrease, the test and validation errors could not converge in the same manner.
During the first phase of learning, both training and validation errors decrease, but, from a given epoch (the ES epoch) onward, the validation error increases as the network starts to overfit, while the training error continues to decrease.
Although this homopolymer error rate is lower than that reported by 454, we expect this error rate to decrease significantly in the future [28], [32].
The auxiliary inverter is utilized to eliminate the current error and to decrease the THD value.
It is observed that error continues to decrease till about 140 epochs and then it becomes almost constant.
The required sample size was increased to avoid Type II sampling error, and to decrease the effect of confounding variables and increase the precision of the study.
Expiratory to inspiratory ratio and the difference between these values were calculated to characterize CT air trapping in order to overcome error related to decrease in lung density due to alveolar enlargement likely associated with age [ 26- 28].
For example, species tree estimation error tends to decrease for all methods with decreases in the ILS level, increases in gene sequence lengths, and increases in numbers of genes.
