Osman Dag [aut, cre], Erdem Karabulut [aut], Reha Alpar [aut], Merve Kasikci [ctb]

Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
selecting an optimal structure of model or network.
The package includes GMDH Combinatorial, and GMDH MIA (Multilayered Iterative Algorithm) using PRESS (Predicted
Residual Error Sum of Squares Statistic) criteria. It is calculated as the sums of squares of the prediction
residuals for those observations.
An introduction of GMDH algorithms:
Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292>
Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.

Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
selecting an optimal structure of model or network.
The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.
An introduction of GMDH algorithms:
Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292>
Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.