关键词: LSTM-FC, 时空序列, 瓦斯浓度, 神经网络

Abstract:

Due to the traditional prediction method of gas concentration in working face using only temporal characteristics of gas data, lacking of prior information related to space, in this paper, using the space-time characteristics of gas data, the prediction model of LSTM-FC（Long Short Time Memory-Full Connection） gas concentration time-space series is constructed by combining long short time memory with full connection neural network. LSTM can solve the problem of long-term dependence gas sequence. Fully connection neural network can accurately capture the spatial correlation of gas sequences. The combination of the two can deeply dig the space-time characteristics of gas data. Then the gas distribution diagram of the working face is constructd by predicting the gas values at different locations. The experimental results show that, by using LSTM-FC model, the prediction error is significantly reduced, and the prediction accuracy is improved compared with other neural network prediction models.

Key words: LSTM-FC, spatiotemporal sequences, gas concentration, neural network