Coal mine driving face gas emission quantity prediction method based on KPCA-POA-LSTM model

• Main Authors: ZHENG WANBO, WU YANQING, LI JINHAI, LIU CHANGHAO, CHEN HUIMIN, WANG YAO, RAN QIHUA, ZHU RONG, SHI YAOXUAN, LI XU, YANG ZHIQUAN, DONG JINXIAO, DONG YINHUAN, LI LEI
• Format: Patent
• Language: Chinese; English
• Published: 13.12.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

The invention relates to a coal mine driving face gas emission quantity prediction method based on a KPCA-POA-LSTM model, and belongs to the technical field of coal mine driving face gas prediction. The method comprises the following steps: carrying out dimensionality reduction and initialization on nonlinear coal mine driving working face gas outburst data by using kernel principal component analysis; using a peacock optimization algorithm to optimize nodes of the long-short-term memory network, so that parameters of the long-short-term memory network are continuously distributed again; constructing a multi-dimensional state matrix, performing feature mapping on the multi-dimensional state matrix by using a long short-term memory network, and selecting sigmoid as an activation function and Adam as a solver; and predicting the gas data of the coal mine driving working face by using the optimized long-short-term memory network. The method can accurately predict the gas emission amount of the coal mine driving