<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Generalization of Patterns by Identification with Polynomial Neural Network

Department of Informatics, Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/1, 010 01 Žilina, Slovakia

Journal of Electrical Engineering

{"article-title":"Generalization of Patterns by Identification with Polynomial Neural Network"}

Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output....

Generalization of Patterns by Identification with Polynomial Neural Network

Online veröffentlicht: 07. Juni 2011

Seitenbereich: 120 - 124

DOI: https://doi.org/10.2478/v10187-010-0017-4

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

This content is open access.

Generalization of Patterns by Identification with Polynomial Neural Network

Online veröffentlicht: 07. Juni 2011

Seitenbereich: 120 - 124

DOI: https://doi.org/10.2478/v10187-010-0017-4

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

This content is open access.

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