Sentence examples for variable predictive models from inspiring English sources

Then SRCMFE is used to the dynamical complexity analysis of mechanical vibration signals and based on that a new fault diagnosis approach for rolling bearing is put forward by combining SRCMFE with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension and the recently proposed variable predictive models based class discrimination (VPMCD) method for fault pattern recognition.

Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification.

Secondly, GA-VPMCD method is presented by combination genetic algorithm (GA) with conventional variable predictive model based class discriminate (VPMCD) approach.

The classifiers adopted in this paper are fuzzy neural networks (FNN), variable predictive model based class discrimination (VPMCD) method and support vector machine (SVM).

Variable predictive model class discrimination (VPMCD) is a conventional pattern recognition method; however, in practice, when the fault diagnosis method is applied to small samples or in multi-correlative feature space, the stability of the VPM constructed based on the least squares (LS) method is not sufficient.

In addition, in the design of the classifier, targeting the limitation of existing pattern recognition method, a new pattern recognition method-variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification.

We compare the performance of these models in order to determine the most important variables in predictive models by taking the relationship among variables into consideration.

This might offset any advantage of the use of such variables in predictive models.

All of the morbidity groups or prescription groups measured by those instruments were treated as dichotomous variables in predictive models.

To examine whether ANC or NLR data can provide additional prognostic power when used with basic clinical variables, we built predictive models by integrating clinical variables with ANC or/and NLR data using the statistical method described in [ 19].

In this paper, we analyze a large volume of historical PCB design data, extract some important variables, and develop predictive models based on the extracted variables using a data mining approach.