Структурне моделювання стійке до викидів у вхідних та залежних змінних

Abstract

This paper describes advances in the algorithm development designed to solve a task of optimal polynomial model selection on multivariate data sets in presence of outliers in both explanatory and response variables. On one side novel algorithm, as its ancestor, is based on GMDH-type PNN, which gives him an universal model structure identification abilities thanks to the evolving adaptively synthesized bounded network. And on the other side the algorithm is enhanced with GM-estimator used for parameter search which allows him achieve robustness to outliers in both explanatory and response variables. Enhanced RPNN demonstrated robustness to outliers in both explanatory and response variables and good accuracy of the automatic structure syntheses.