Applications of neural networks for free unfolding of experimental data from fusion neutron spectrometers

被引：0

作者：

Ronchi, E. ^{[1
]}

Conroy, S. ^{[1
]}

Sunden, E. Andersson ^{[1
]}

Ericsson, G. ^{[1
]}

Johnson, M. Gatu ^{[1
]}

Hellesen, C. ^{[1
]}

Sjostrand, H. ^{[1
]}

Weiszflog, M. ^{[1
]}

机构：

[1] JET EFDA, Culham Sci Ctr, Abingdon OX14 3DB, Oxon, England

来源：

COMPUTATIONAL INTELLIGENCE IN DECISION AND CONTROL | 2008年 / 1卷

关键词：

D O I：

10.1142/9789812799470_0005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Free unfolding in neutron spectroscopy means reconstructing energy spectra from experimental data without a priori assumptions regarding their shape. Due to the ill-conditioned nature of the problem, this cannot be done analytically. Neural networks (NN) are here applied to this task and synthetic data is used for training and testing. Results showed very consistent performance especially in the region of low and medium counts, where they fall near the Poisson statistical boundary. Comparison with other unfolding methods validated these results. Application time on the order of mu s makes NN suitable for real-time analysis. This approach can be applied to any instrument of which the response function is known.

引用

页码：29 / 35

页数：7

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