Self-Organizing Methods in Modeling: Gmdh Type Algorithms

By S. J. Farlow. 1984

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Deep learning in neural networks: An overview (2014, methodology): “There have been numerous applications of GMDH-style nets, e.g. Farlow (1984), Ikeda, Ochiai, and Sawaragi (1976), Ivakhnenko (1995), Kondo (1998), Kondo and Ueno (2008), Kordík, Náplava, Snorek, and Genyk-Berezovskyj (2003), Madala and Ivakhnenko (1994) and Witczak, Korbicz, Mrugalski, and Patton…”
Constructive algorithms for structure learning in feedforward neural networks for regression problems (1997, methodology): “Constructive algorithms in this category [35], [49] are inspired by GMDH developed by Ivakhnenko [129].”
Machine Learning, Neural and Statistical Classification (2009, methodology): “I'd suggest Polynomial Networks (Farlow, 1984; Elder & Brown, 1994), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (Hastie & Tibshirani, 1990), multilinear trees (e.g., OC1; Murthy et al., 1994), and piecewise methods such as Constrained Topological Maps (Mulier, 1994)”
A novel comprehensive experimental study concerned synthesizes and prepare liquid paraffin-Fe3O4 mixture to develop models for both thermal conductivity & viscosity: A new approach of GMDH type of neural network (2019, background): “The general relationship between inputs and outputs by the discrete and complex relationship of the Volterra functional series which known as Kolmogorov-Gabor polynomial can be presented as follows [60]:”