根据KPCA-ISSA-KELM的采矿水冲入源歧视模型
Wei Wang1,2, Xinchao Cui2, Yun Qi1,2,3
1College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, P.R. China.
PloS one
|June 3, 2024
概括
一个新的模型结合了内核主要组件分析 (KPCA) 和改进的子搜索算法 (ISSA) 优化的内核极端学习机器 (KELM),准确地识别了矿井水的冲动来源,提高了煤炭开采操作的安全性.
科学领域:
- 地质技术工程 地质技术工程
- 采矿领域的人工智能
- 防止水危害 防止水危害
背景情况:
- 矿井水冲入事故对煤矿安全和生产构成重大风险.
- 准确识别冲入水源对于有效的预防策略至关重要.
- 现有的模型往往缺乏现实应用所需的准确性和稳定性.
研究的目的:
- 开发一个非常准确的模型来识别矿井水的冲进源.
- 改进防止煤矿冲撞事故中的水污染.
- 提高水源识别模型的可靠性和稳定性.
主要方法:
- 使用内核主要组件分析 (KPCA) 来减少数据的维度.
- 开发了一种改进的搜索算法 (ISSA),结合了正弦混沌映射,动态适应权重和可西变化与反向学习.
- 优化的内核极端学习机 (KELM) 参数使用ISSA创建KPCA-ISSA-KELM模型用于水源歧视.
主要成果:
- 与其他模型 (KPCA-SSA-KELM,KPCA-KELM,ISSA-KELM,SSA-KELM,KELM) 相比,KPCA-ISSA-KELM模型在识别矿井入水源方面表现出更高的准确性.
- 与比较模型相比,准确度的提高范围从4.17%到25%不等.
- 在山西的实际应用中,KPCA-ISSA-KELM模型实现了最低的误判率 (4.76%).
结论:
- 拟议的KPCA-ISSA-KELM模型在采矿水冲动源识别方面取得了重大进展.
- 该模型具有出色的通用性,稳定性和准确性,使其适用于实际的矿山安全应用.
- 这项研究提供了一种可靠的方法来防止水冲事故,从而确保更安全的煤矿生产.
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