Constructing general partial differential equations using polynomial and neural networks
type: Item
instance of: scholarly article
title: Constructing general partial differential equations using polynomial and neural networks
zbMATH Open document ID: 1402.35274
author: Ladislav Zjavka
author: Witold Pedrycz
published in: Neural Networks
publication date: 2018-08-30T00:00:00Z
review text: The aim of the paper is to introduce and develop the concept of a differential polynomial neural network, D-PNN. One starts with some background considerations on partial derivative terms substitution and GMDH polynomial neural networks and one describes the principles of D-PNN. Further, the D-PNN multi-layer architecture is developed. The algorithm has been implemented and numerical examples for instance for function approximations are presented. Comparisons of D-PNN and other classical ANN implementation results are shown and discussed.
reviewed by: Claudia Simionescu-Badea
Mathematics Subject Classification ID: 35Q68
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 6929424
zbMATH Keywords: artificial neural networks
zbMATH Keywords: differential polynomial neural network
zbMATH Keywords: group method of data handling (GMDH) networks
zbMATH Keywords: function approximation
MaRDI profile type: Publication
full work available at URL: j.neunet.2015.10.001
OpenAlex ID: W2188548788
Wikidata QID: Q40345584
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DBLP publication ID: journals/nn/ZjavkaP16
DOI: 10.1016/J.NEUNET.2015.10.001
description: scientific article; zbMATH DE number 6929424