Polluted gas quantitative detection in multi-gas sensor based on bidirectional long-short term memory network

Quantitative detection of polluted gas by electronic nose can reduce the cost of detection and improve the efficiency of measurement. Through the effective pattern recognition method, the electronic nose can analyse the continuous periodic data and realise the detection of specific tasks. In this paper, the pollution gas concentration prediction method based on bidirectional long-short term memory network (Bi-LSTM) is proposed. And the effect of the Bi-LSTM model with different time steps, hidden layers and different combinations of sensor features on the performance of pollution gas prediction model is investigated. This method can extract deep features by automatically learning the gas response information of the sensor array, and its performance is better. The proposed method is verified on the air quality dataset, which proves that the proposed method has high accuracy in the quantitative detection of gas concentration based on electronic nose information.