Research progress on neural network algorithms for mixed gas detection in coal mines
| Autor: | JIAO Mingzhi, SHEN Zhongli, ZHOU Yangming, HE Xinjian, HE Yaoyi |
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| Jazyk: | čínština |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Gong-kuang zidonghua, Vol 49, Iss 9, Pp 115-121 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1671-251X 1671-251x |
| DOI: | 10.13272/j.issn.1671-251x.18105 |
| Popis: | When coal mine gas sensors are used for mixed gas detection, there is cross interference between measurement signals. It is difficult to ensure detection accuracy. For the same gas to be identified, the recognition precision of traditional gas recognition algorithms is lower than that of gas recognition algorithms based on neural networks. Neural networks achieve higher gas recognition accuracy by adjusting their network layers, the number of neurons in each layer, the activation function of neurons, and the weights between each layer of networks. This paper introduces the structure of a coal mine mixed gas detection system. By constructing a gas sensor array, utilizing its multi-dimensional gas response mode, and combining specific gas recognition algorithms, the qualitative and quantitative recognition of mixed gases is achieved. Several neural network algorithms for mixed gas detection in coal mines are analyzed and compared. The algorithms mainly include backpropagation (BP) neural network, convolutional neural network (CNN), recurrent neural network (RNN), and radial basis function (RBF) neural network. BP neural network can usually achieve high classification precision, but requires training a large number of parameters and a long training time. Usually, in order to reduce time and improve precision, BP neural networks can be combined with other algorithms. CNN can automatically extract data features, with better precision and training speed than BP neural networks. But it is prone to falling into local optima. RNN can use less data and extract more effective features, but it is prone to problems such as gradient vanishing. RBF neural networks have strong robustness and online learning capability, but they usually require a large amount of data to complete model training. The application of neural network algorithms will significantly improve the detection precision of mixed gases in coal mines, ensuring the implementation of intelligent coal mines. |
| Databáze: | Directory of Open Access Journals |
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