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Use of neural network based auto-associative memory as a data compressor for pre-processing optical emission spectra in gas thermometry with the help of neural networkстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 18 июля 2013 г.
- Авторы: Dolenko S.A., Pal A.F., Persiantsev I.G., Serov A.O., Filippov A.V.
- Журнал: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
- Том: 502
- Номер: 2-3
- Год издания: 2003
- Издательство: Elsevier BV
- Местоположение издательства: Netherlands
- Первая страница: 523
- Последняя страница: 525
- DOI: 10.1016/S0168-9002(03)00489-3
- Аннотация: Determination of temperature from optical emission spectra is an inverse problem that is often very difficult to solve, especially when substantial noise is present. One of the means that can be used to solve such a problem is a neural network trained on the results of modeling of spectra at different temperatures (Dolenko, et al., in: I.C. Parmee (Ed.), Adaptive Computing in Design and Manufacture, Springer, London, 1998, p. 345). Reducing the dimensionality of the input data prior to application of neural network can increase the accuracy and stability of temperature determination. In this study, such pre-processing is performed with another neural network working as an auto-associative memory with a narrow bottleneck in the hidden layer. The improvement in the accuracy and stability of temperature determination in presence of noise is demonstrated on model spectra similar to those recorded in a DC-discharge CVD reactor. (C) 2003 Elsevier Science B.V. All rights reserved.
- Добавил в систему: Доленко Сергей Анатольевич