GMDHreg: Regression using GMDH Algorithms

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.

Getting started

Package details

AuthorManuel Villacorta Tilve
MaintainerManuel Villacorta Tilve <[email protected]>
LicenseGPL-3
Version0.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("GMDHreg")

Try the GMDHreg package in your browser

Any scripts or data that you put into this service are public.

GMDHreg documentation built on May 1, 2019, 7:32 p.m.