Hence, a closed loop simulation study was performed to see the differences between the performance of model predictive controller using previously estimated linear and nonlinear models and a standard proportional integral (PI) controller.
Often a modeller will choose a trade-off between the performance of the model and the interpretability of the prediction according to the purpose of making a prediction.
To estimate the optimism, we fitted models to each of these bootstrapped samples and calculated the difference between the performance of the model on the bootstrapped sample and its performance on the original sample.
Figure 5B,C,E,F shows excellent agreement between the performance of the model and the data in Figure 4B,C,E,F.
The agreement between predictions of the proposed energy performance model and measurement results is presented to be very good.
The log-likelihoods in Table 1 show that the MGR model was better than the Cbv model in four of the eight cases, which means there is little difference between the performance of the models.
The approach adopted here is the investigation of the relation between material model and performance model as well as main drivers of building material research: idea, performance, research tools development, application and technological evolution.
Consequently, not much difference was observed between the performance of the original model and the model constructed using our approaches.
Little difference was observed between the performance of LUR and UK models in predicting concentrations.
Additionally, congruencies between the performance of RNA models and the results of the AICc modeltest can be drawn from our results.
