Analysis of neutron noise spectra using neural networks.

機譯：用神經(jīng)網(wǎng)絡(luò)分析中子噪聲譜。

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Neural network architectures based on the back-propagation paradigm have been developed to recognize the features, and detect resonance shifts in, power spectral density (PSD) data. Our goal is to advance the state of the art in the application of noise analysis techniques to monitor nuclear reactor internals. The initial objectives have been to use PSD data, acquired over a period of about 2 years by PSDREC (power spectral density recognition system), to develop neural networks that are able to differentiate between normal neutron power spectral density data and anomalous spectral data, and detect significant shifts in the positions of spectral resonances while reducing the effect of small shifts. Neural network systems referred to in this paper as spectral feature detectors (SFDs) and integral network filters have been developed to meet these objectives. The performance of the SFDs is the subject of this paper. 2 refs., 2 figs.

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Korsah, K.; Uhrig, R. E.;
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年度 1991
頁碼 1-10
總頁數(shù) 10
原文格式 PDF
正文語種 eng
中圖分類工業(yè)技術(shù);
關(guān)鍵詞
Neutron Spectra; Data Analysis; Monitoring; Neural Networks; Neutron Detectors; Noise; Nuclear Power Plants; Performance; Spectral Density; EDB/220100; EDB/990200;

機譯：中子譜;數(shù)據(jù)分析;監(jiān)測;神經(jīng)網(wǎng)絡(luò);中子探測器;噪聲;核電站;性能;光譜密度; EDB / 220100; EDB / 990200;

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機譯：用神經(jīng)網(wǎng)絡(luò)分析中子噪聲譜

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Analysis of neutron noise spectra using neural networks.

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