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In this study, based on the updated model using response surface methodology, a practical model validation methodology via uncertainty propagation is presented.
Firstly, we propose a dynamic extension of the popular moving average rule and enhance it with a model validation methodology using heat maps to analyse favourable profitability in specific holding time and signal regions.
Current model validation methodology is confined to linear models and is primarily concerned with (i) correcting inaccurate model parameters and (ii) ensuring that sufficient elements are included for these cases, using measured data.
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This article also includes discussion about simulation validation methodologies.
The methodology in Chapter 2, Model development and validation methodology: a classical big data application, is used to evaluate and rank the models indicating which six models are recommended for future use.
The model validation shows that this methodology can truly capture macro-behaviors of these struts.
The aim of this article is to detail the construction of an EAC model including: descriptions of model structure and inputs; calibration endpoints and methodology; and model validation.
The validation methodology and the model itself contribute towards a better understanding of modelling RE processes.
This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model.
We believe that our approach can serve as a preprocessing step for model integration and the rule-based validation methodology can be applied to other ontology formats.
Given the small size of the data set (51 patients), we used the leave-one-out cross validation methodology, constructing logistic regression models to predict the grade of each patient, and training each model using the other patients.
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