Improved deep bidirectional recurrent neural network for learning the cross-sensitivity rules of gas sensor array

文献类型： 外文期刊

第一作者： Wang, Zhen

作者： Wang, Zhen;Li, Yanhao;He, Xiangnan;Yan, Rui;Li, Zhemin;Li, Xian;Jiang, Yadong;Li, Zhemin

作者机构：

关键词： Gas sensor array; Deep BRNN; Bidirectional learning ability; Cross-sensitivity; Pattern recognition

期刊名称：SENSORS AND ACTUATORS B-CHEMICAL （ 影响因子：8.4； 五年影响因子：7.2 ）

ISSN：

年卷期： 2024 年 401 卷

页码：

收录情况： SCI

摘要： Cross-sensitivity among chemical gas sensors leads to inaccurate identification of mixed gas. Pattern recognition algorithms are usually applied to improve the recognition accuracy. However, the abilities of current methods to process sequence property of response data are not strong enough. Especially, they cannot deal with bidirectional cross-sensitive issue, leading to recognition errors. Bidirectional Recurrent Neural Network (BRNN) could well learn bidirectional association features between word sequences in natural language processing field, which is similar to the bidirectional interaction of cross-sensitivity. In this study, an improved deep BRNN model was constructed to solve the cross-sensitivity problem of chemical gas sensor array. A chemical gas sensor array with four units was fabricated and response data was thoroughly obtained. Data preprocessing methods, model structure hyperparameters and optimizers were studied. Finally, an improved deep BRNN model was developed with 3 layers and 100 hidden_size, training with Adamax optimizer. A recognition accuracy of 98.93% was achieved, attributing to the model's excellent learning ability to the bidirectional cross-sensitivity rules among gas sensors. This improved BRNN model provided a novel idea to eliminate cross-sensitivity, exhibiting good potential for recognizing mixed gas analyte accurately.

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