基于三层神经网络的单片电子鼻微阵列传感器数量优化

V. Sysoev, V. Y. Musatov, A. V. Silaev, T. R. Zalyalov
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引用次数: 1

摘要

我们测试了基于金属电极分割的单片SnO2薄膜的“电子鼻”型多传感器微阵列的气体响应。通过三层神经网络,我们发现并不是所有的传感器段都需要参与到成功的气体识别中。通过正确选择传感器,可以在不损失识别能力的情况下显著减少传感器的数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Optimization of Number of Sensors in One-Chip Electronic Nose Microarrays with the Help of 3-Layered Neural Network
We have tested the gas response of multisensor microarrays of "electronic nose" type based on monolithic SnO2 film segmented by metal electrodes. With 3-layered neural network we show that not all the sensor segments of the film are necessary to be involved for successful gas identification. By proper choosing the sensors it is possible to significantly decrease the number of sensors without a loss of recognition power.
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