Polynomial Neural Network (PNN) Algorithms

Overview

Polynomial Neural Network (PNN) algorithm - GMDH-type neural network - one of the most promising methods developed by PNN Research team. The goal of the algorithm application is to extract knowledge from experimental data and to determine its best mathematical description. The proposed method can be used to analyze complex data sets with the aim to determine internal data relationships and to present knowledge about these relationships in the form of mathematical description (polynomial regression). As an examples of possible application area of PNN algorithm one can consider any sphere where sets of observation data should be analyzed and data relationships models should be build. There are, for example, chemistry (QSAR), economical systems analyses, stocks and financial market instruments, insurance risks study, medical diagnostics, etc.

PNN - a self-organizing multi-layered iterative algorithm that automatically provides linear and non-linear polynomial regression models. The PNN embodies the advantages of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs) into a single entity. It can model both linear and non-linear relationships like ANNs, and it yields a polynomial regression equation like MLR for easy interpretation. This algorithm provides robust results in the presence of correlated and irrelative variables or/and outliers. The results of this algorithm can be easily interpreted. For more details please visit our PNN algorithm related site