Sentence examples for model complex relationships from inspiring English sources

An artificial neural network (ANN) is a powerful mathematical framework used to either model complex relationships between inputs and outputs or find patterns in data.

Here we present the mechanistic insight derived from a mathematical structure ("wiring diagram") used to model complex relationships between a highly relevant (p = 9.43 × 10− 12) global "cancer network" of 32 genes and their known links.

They can be used to model complex relationships between inputs and outputs.

ANFIS has been applied in many aspects of mineral processing to model complex relationships (For flotation, ANFIS is applied to the prediction of collision probability, recovery, gas holdup, diameter, and surface area flux of bubbles) (Jorjani et al. 2008, 2009; Chelgani et al. 2010, 2011a, b; Chelgani and Makaremi 2013; Shahbazi et al. 2013a, b).

It has been widely established that to model complex relationships, when regression (linear or non-linear) cannot accurately correlate variables, soft computing methodology can effectively be used, as this technique is developed to exploit tolerance for imprecision, uncertainty, and partial truth.

Furthermore, new network analysis and visualization techniques are being used more frequently to model complex relationships of scientific output.

MARS is a multivariate, piecewise regression technique that can be used to model complex relationship.

Multilayer perceptron is a popular ANN architecture with back propagation, a class of supervised neural network and can be used to model complex relationship between inputs and outputs [36, 41].

We have tried to model complex relationship between the genotypes and occurrence of DFU.

As ANN are non-linear statistical data modeling tools their achievement for modeling complex relationships between inputs and outputs or to find patterns in data are more successful than statistical methods.

A semiparametric GAM was used since it is well-suited to modeling complex relationships where an underlying nonlinear relationship is not known a priori (Wood 2006).