基于长短期记忆递归神经网络的化学物质分类

2017 IEEE 17th International Conference on Communication Technology (ICCT) Pub Date : 2017-10-30 DOI:10.1109/ICCT.2017.8359978

Jinlei Zhang, Junxiu Liu, Yuling Luo, Qiang Fu, Jinjie Bi, Senhui Qiu, Yi Cao, Xuemei Ding

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引用次数: 4

摘要

提出了一种基于循环神经网络长短期记忆的化学物质检测方法。化学物质的数据是用质谱仪收集的，这是一个时间序列数据。LSTM-RNN分类器的分类准确率为96.84%，高于普通前馈神经网络的75.07%。实验结果表明，LSTM-RNN能够学习化学物质数据集的特性，达到较高的检测精度。

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Chemical substance classification using long short-term memory recurrent neural network

This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.

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2017 IEEE 17th International Conference on Communication Technology (ICCT)

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