Separate Source Predictive Control of Gas Emission based on psobp AdaBoost
- DOI
- 10.2991/ahis.k.220601.051How to use a DOI?
- Keywords
- gas emission; particle swarm optimization algorithm; adaboost iterative algorithm
- Abstract
Gas disaster is a great challenge for coal mine enterprises to work safely. Once gas disaster happens, people’s life and property safety will face great threat. High precision prediction of gas emission is an important measure to improve gas prevention and gas control, and it is of great practical significance to improve the safety of production and people’s life and property safety of mining enterprises. Therefore, this paper discusses a new method of gas emission prediction, and proposes a prediction model of gas emission quantity based on psobp adaboost algorithm, which combines bp neural network, particle swarm optimization algorithm, adaboost iterative lifting algorithm and gas emission source separation prediction method with nonlinear mapping characteristics.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license.
Cite this article
TY - CONF AU - Tao Xu AU - Tong Cheng PY - 2022 DA - 2022/06/02 TI - Separate Source Predictive Control of Gas Emission based on psobp AdaBoost BT - Proceedings of the 2021 International conference on Smart Technologies and Systems for Internet of Things (STS-IOT 2021) PB - Atlantis Press SP - 268 EP - 273 SN - 2589-4919 UR - https://doi.org/10.2991/ahis.k.220601.051 DO - 10.2991/ahis.k.220601.051 ID - Xu2022 ER -