使用近红外反射光谱的PAIPSO-ELM精确的煤炭分类
Yiyang Wang1, Boyan Li2,3, Haoyang Li2,3
1School of Electrical and Automation Engineering, Liaoning Institute of Science and Technology, 117004 Benxi, China.
ACS omega
|December 9, 2024
概括
本研究介绍了一种使用近红外光谱和优化机器学习的先进煤炭分类方法. 位置适应性惯性粒子群集优化-极端学习机器 (PAIPSO-ELM) 模型显著提高了煤炭资源利用的准确性和效率.
科学领域:
- 地质科学和材料科学 材料科学
- 计算智能和机器学习
背景情况:
- 传统的煤炭分类方法在准确性和效率方面存在局限性.
- 不高效的煤炭利用源于分类的不准确性.
- 近红外反射光谱 (NIRS) 提供了煤炭分析的潜力.
研究的目的:
- 开发一种新的,准确和高效的煤炭分类方法.
- 克服传统煤炭分类技术的局限性.
- 加强利用中国庞大的煤炭储备.
主要方法:
- 从煤样本收集近红外反射光谱 (NIRS) 数据.
- 应用极端学习机器 (ELM) 进行初始分类.
- 使用粒子优化 (PSO) 来优化ELM参数.
- 开发了一个改进的位置适应性惯性PSO-ELM (PAIPSO-ELM) 模型.
主要成果:
- 该ELM模型显示了良好的初始分类性能.
- 与基本的ELM相比,PSO-ELM模型的分类准确度提高了9.68%.
- 在没有增加训练时间的情况下,PAIPSO-ELM模型实现了额外的2%的准确性改进.
- 该PAIPSO-ELM模型在克服局部最佳状态方面表现出卓越的性能.
结论:
- 该PAIPSO-ELM模型为煤炭光谱分类提供了有效的解决方案.
- 这种方法满足了工业对煤炭分类高精度和速度的需求.
- 拟议的方法提高了煤炭资源的有效利用.
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