Powerful analytical tool able to deal with large amount of numeric data. It will help you to discover hidden internal relations and build structural and forecasting analytical models in the form of simple polynomial equations.
Knowledge Discovery and Data Mining with GMDH-type Polynomial Neural Network algorithm.
Get your data structure presentation as simple as a+b*x1+c*x3+a*x43.
Proposed algorithms and software will help you to deal with large amount of numeric data, discover hidden internal relations and build structural and forecasting analytical models in the form of simple polynomial equations.
Polynomial Neural Network (PNN) provides robust nonlinear polynomial regression identification for the numerical data with unknown dependencies. It is based on modified GMDH-type Neural Networks and characterized by the highest prediction ability. Moreover it is insensible to outliers and irrelevant variables, provides fast learning and numerical stability.
Let's compare PNN with Artificial Neural Network (ANN) algorithms, which also can be used to model complex non-linear relationship. The serious disadvantage of ANN method is that the all dependencies (between parameters and responses) are hidden within neural network structure and therefore the interpretation of calculated results is difficult. Besides that an essential time of learning make difficulties for using ANN in real time system for modeling and forecasting. GMDH-type Neural Networks allows restoring the unknown nonlinear regression in parametric form (as an equation). But original GMDH algorithms in general rather sensitive to outliers. Methods of robust modeling insensitive to outliers was developed in the linear case only and characterized by significant time of calculation when the structure of model is unknown a priory.
Polynomial Neural Network
- Allows identifying robust nonlinear polynomial models of unknown structure
- Is insensible to outliers
- Is characterized by numerical stability and high speed of learning that allows using in real time system