advanced
Journal Information
Journal Information
   Description
   Editorial Board
   Guide for Authors
   Ordering

Contents Services
Contents Services
   Regular Issues
   Special Issues
   Authors Index

Links
Links
   FEI STU Bratislava
   SAS Bratislava

   Feedback

[2, 2010] 

Journal of Electrical Engineering, Vol 61, 2 (2010) 120-124

GENERALIZATION OF PATTERNS BY IDENTIFICATION WITH POLYNOMIAL NEURAL NETWORK

Ladislav Zjavka

   Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

Keywords: polynomial neural network, dependence of variables identification, rational fractional functions, function approximation, differential equation, modelling of complex systems


[full-paper]


© 1997-2019  FEI STU Bratislava