Figure - uploaded by Md Fahmi Abd Samad
Content may be subject to copyright.
Variables, terms and parameter values of selected model by PMIC2 and PMIC for gas furnace data.

Variables, terms and parameter values of selected model by PMIC2 and PMIC for gas furnace data.

Source publication
Article
Full-text available
Model structure selection is one among crucial steps in system identification and in order to carry out this, an information criterion is needed. It plays an important role in determining an optimum model structure with the aim of selecting an adequate model to represent a real system. A good information criterion should not only evaluate predictiv...

Contexts in source publication

Context 1
... than the calculated direct current level (which is equivalent to a constant), the variables selected are 1, 2 and 22. From Jamaluddin et al. (2007) and Samad (2017), PMIC selected the model [111 010 010 000 000] as the best chromosome. The selected regressors and its parameter values selected by both PMIC2 and PMIC are provided in Table 1. Comparisons could be made directly, where it shows that PMIC2 selected a more parsimonious model than PMIC. ...
Context 2
... than the calculated direct current level (which is equivalent to a constant), the variables selected are 1, 2 and 22. From Jamaluddin et al. (2007) and Samad (2017), PMIC selected the model [111 010 010 000 000] as the best chromosome. The selected regressors and its parameter values selected by both PMIC2 and PMIC are provided in Table 1. Comparisons could be made directly, where it shows that PMIC2 selected a more parsimonious model than PMIC. ...

Similar publications

Preprint
Full-text available
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks. Recent studies have proposed to use neural networks with differentiable convex optimization layers to implement model predictive controllers. The motivation is to replace real-time optimization i...

Citations

... Once a model has been identified, the parameter has been estimated using the least squares method. The OF used in evaluating the optimality of model has been Parameter Magnitude-based Information Criterion 2 (PMIC2) written as follows [28], [29]: ...
... The theoretical idea of AIC is based on Kullback-Leibler information and maximum-likelihood estimation theory, while BIC is developed from Bayesian arguments and it is related to Bayes factors. Another information criterion is known as parameter magnitude-based information criterion (PMIC), and later developed into PMIC2 [3]. Figure 2 provides a guide on how an information criterion may be developed. ...
... The theoretical idea of AIC is based on Kullback-Leibler information and maximum-likelihood estimation theory, while BIC is developed from Bayesian arguments and it is related to Bayes factors. Another information criterion is known as parameter magnitude-based information criterion (PMIC), and later developed into PMIC2 [3]. Figure 2 provides a guide on how an information criterion may be developed. ...