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Like all previous research we were unable to identify a model that fitted the data well, although a number of modelling components appeared to be useful in improving model fit to the data, and therefore determine our conclusions regarding the factorial validity of Ryff's measures, with reference to her theory, and in regard to these 42 items.
Neural network with a specific restricted connectivity structure is used to identify a model of a real-life process.
But if we identify a model with its description, then each new description yields a new model, which seems to be counterintuitive.
It is explained how to identify a model, approximating in the best-possible way the general linear behaviour of the system.
In the current control design approaches, measured data are used to first identify a model of the plant, then a controller is designed based on the identified model.
A more realistic aim is therefore to identify a model that provides an acceptable approximation, in the context of the application in which it is used.
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We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound.
The second method identifies a model for which the Pareto point is the closest (based on Euclidean distance) to a centroid of all points in the Pareto neighbourhood.
The bivariate analysis identified a model able to predict fluid responsiveness in 91 % of patients (AUC 0.92; Y = 24.9 + PPV*1.243 + Pdic*0.911 + CCEnor*0.004).
Taking into consideration the hydrochemical characteristics of this pit lake and the spatial distribution of the layers identified, a model that explains its seasonal limnological evolution is presented.
The data lifting approach consists of two steps, identifying a model with a slow/lifted sampling period and extracting a fast model.
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