感知器、馈入和深度前馈人工神经网络在光谱数据上的应用:天体物理和聚变等离子体

IF 0.4 4区 物理与天体物理 Q4 ASTRONOMY & ASTROPHYSICS
N. Sakan, I. Traparić, V. Srećković, M. Ivković
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引用次数: 0

摘要

人工神经网络在解决各种数据分析和分类问题方面的复杂问题方面势头越来越猛。因此,决定它们在复杂等离子体上的可用性的想法出现了。用于分析的输入数据的选择是一组恒星光谱数据。它由在各种条件下的复杂成分等离子体组成,这取决于恒星的类型,通过校准的标准化程序和设备进行测量。分析结果表明,即使是简单类型的感知器人工神经网络,也可以为复杂成分的光谱分析带来可接受的质量结果。所分析的人工神经网络在有限的数据集上表现良好。这些结果可以被解释为在各种应用中进一步发展复杂神经网络的优点,例如在天体物理和聚变等离子体中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The usage of perceptron, feed and deep feed forward artificial neural networks on the spectroscopy data: astrophysical & fusion plasmas
. Artificial neural networks are gaining a momentum for solving complex problems in all sorts of data analysis and classification matters. As such, idea of determining their usability on complex plasma came up. The choice for the input data for the analysis is a set of stellar spectral data. It consists of complex composition plasma under vast variety of conditions, dependent on type of star, measured with calibrated standardized procedures and equipment. The results of the analysis has shown that even a simple type of perceptron artificial neural network could lead to results of acceptable quality for the analysis of spectra of complex composition. The analyzed ANNs performed good on a limited data set. The results can be interpreted as a figure of merit for further development of complex neural networks in various applications e.g. in astrophysical and fusion plasmas.
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来源期刊
CiteScore
1.10
自引率
20.00%
发文量
4
审稿时长
>12 weeks
期刊介绍: Contributions of the Astronomical Observatory Skalnate Pleso" (CAOSP) is published by the Astronomical Institute of the Slovak Academy of Sciences (SAS). The journal publishes new results of astronomical and astrophysical research, preferentially covering the fields of Interplanetary Matter, Stellar Astrophysics and Solar Physics. We publish regular papers, expert comments and review contributions.
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