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ANN is a multivariate calibration method capable of modeling complex functions.
"Neural networks are advanced modeling techniques, which are capable of modeling complex functions.
Artificial Neural Networks (ANNs) [22] are flexible non linear mathematical systems capable of modeling complex functions.
A neural network is composed of a set of highly interconnected nodes, and is a type of non-parametric regression approach for modeling complex functions [ 23, 24].
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GP regression is flexible and capable of modelling complex functions, as opposed to the restrictive form of the polynomial models that are used in traditional RSM.
In this study an architecture for modeling complex systems in function approximation and regression was used, based on using adaptive neuro-fuzzy inference system (ANFIS).
Zhang et al. tested using forcing functions when modeling complex, multiresponse PD systems.
This new solution benefits from the ANN's ability to model complex nonlinear functions to intelligently enhance the spatial reuse while preserving fairness.
To model such complex contextual functions, the decision tree has to be excessively large, but DNNs are capable to model complex contextual factors by employing multiple hidden layers.
To effectively model these fluxes, complex functions that include soil and vegetation properties are often required.
Feed-forward neural network usually has one or more hidden layers and an output layer, which enable the network to model nonlinear and complex functions.
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