Application of Neural Networks for the Measurement of Electronic Temperature in Nuclear Fusion Experiments

Autor:	G. Manduchi, O. Barana
Rok vydání:	2002
Předmět:	Materials science Artificial neural network Computation Electronic temperature Exponential function Computational physics symbols.namesake Artificial Intelligence symbols Langmuir probe Nuclear fusion Plasma diagnostics Current (fluid) Software
Zdroj:	Neural computing & applications info:cnr-pdr/source/autori:Barana O., Manduchi G./titolo:Application of Neural Networks for the Measurement of Electronic Temperature in Nuclear Fusion Experiments/doi:/rivista:Neural computing & applications (Print)/anno:2002/pagina_da:351/pagina_a:/intervallo_pagine:351/volume:10
ISSN:	1433-3058 0941-0643
DOI:	10.1007/s005210200007
Popis:	Langmuir probes are used in nuclear fusion experiments to derive the electronic temperature of a high temperature ionised gas. For this purpose, the current I flowing through the probe at varying applied potentials V is measured. The shape of the resulting V-I characteristic is exponential, and depends upon the electronic temperature. Traditionally, fitting procedures are used to derive the temperature, but the amount of computation required is high as the measurement is repeated many times during the experiment. In this paper the use of neural networks to derive the electronic temperature from Langmuir probe data is investigated. Neural networks proved to be comparable with traditional methods in the accuracy of the reconstruction, though requiring less computational resources.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e305bbd7ecef85b170292a89b82ae42 https://doi.org/10.1007/s005210200007 Zobrazit plný text záznamu Plný text ve formátu PDF