Generalized inverse matrix-recurrent neural network data processing algorithm for multiwavelength pyrometer

Jian Xing; Pengyu Yan; Shuanglong Cui

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Generalized inverse matrix-recurrent neural network data processing algorithm for multiwavelength pyrometer

机译：Generalized inverse matrix-recurrent neural network data processing algorithm for multiwavelength pyrometer

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摘要

A generalized inverse matrix-recurrent neural network (GIM-RNN) data processing algorithm for unknown emissivity was proposed to measure high-temperature by multiwavelength pyrometer (MWP). First, emissivity classification was realized quickly according to a solution from generalized inverse algorithm to underdetermined multiwavelength equation group. According to the relationship between the emissivity of 2 adjacent channels of the 6 channel thermometer used in this paper, 243 emissivity models (1 * 3 * 3 * 3 * 3 * 3) were designed for classification. Twelve of them were shown on the schematic diagram in the text. To make the figure clear and easy to observe, the prediction results of six common emissivity models were listed as the experimental results. Then, it was input into the corresponding RNN subnetwork to inverse temperature precisely. Simulation results showed that in the range of 1500 to 3000 K temperature, the relative error of the test set was within 1.0 for the network trained after classification by GIM, whereas the relative error of the test set was within 1.2 for the network trained without GIM classification. After 5.0 random noise was added to the inputting data, the relative error still was controlled within 1.5, which reflected the good antinoise performance of the algorithm. Multispectral measurement data of rocket engine plumes is processed by the proposed algorithm in this manuscript. The inversion results are consistent with the theoretical results. It is indicated that the proposed algorithm has good adaptability to different materials. It is expected to become a general data processing algorithm for MWP.

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来源
《Optical Engineering》 |2022年第12期|124109-1-124109-14|共14页
作者
Jian Xing; Pengyu Yan; Shuanglong Cui;
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作者单位

Northeast Forestry Univ. (China);

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
Emissivity; Neural networks; Evolutionary algorithms; Data modeling; Data processing; Education and training; Temperature metrology;

Generalized inverse matrix-recurrent neural network data processing algorithm for multiwavelength pyrometer

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