Constructing general partial differential equations using polynomial and neural networks (Q1669297)
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scientific article; zbMATH DE number 6929424
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Constructing general partial differential equations using polynomial and neural networks |
scientific article; zbMATH DE number 6929424 |
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Constructing general partial differential equations using polynomial and neural networks (English)
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30 August 2018
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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.
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artificial neural networks
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differential polynomial neural network
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group method of data handling (GMDH) networks
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function approximation
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