| 引用本文: | 周孟然1,凌 胜2,来文豪1,卞 凯1,朱梓伟1,沈汝涵2.基于黏菌优化极限学习机的煤矸石多光谱识别(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（5）：1-7 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 煤和矸石的精准辨识是煤矸分选和煤炭清洁高效利用的重要前提,针对传统方法存在效率低、需加装辐射
隔离以及受环境干扰等诸多不足,提出了基于多光谱图像特性和光谱特性来识别煤和矸石,构建黏菌优化极限学
习机(Slime Mold Algorithm Extreme Learning Machine,SMA-ELM)的分类模型。 搭建多光谱数据采集系统完成煤与
矸石的光谱图像采集,通过 LBP 对光谱图像进行特征提取并使用 PCA 主成分分析对提取后的特征向量降维,输入
SMA-ELM 分类模型、蚁狮优化极限学习机(Antlion Algorithm Optimized Extreme Learning Machine,ALO-ELM)分类
模型、鲸鱼优化极限学习机(Whale Algorithm Optimized Extreme Learning Machine,WOA-ELM)分类模型进行对比,
重点研究不同波长响应下煤和矸石的辨识精度来筛选最佳波长,通过多评价指标对优化后的最优波段进行比较。
实验结果表明, SMA -ELM 分类效果最佳,第 6 波段为最优波段, SMA - ELM 在该波段的平均识别准确率为
95. 08%,煤和矸石的识别 F1-Score 分别为 96. 47%和 92. 68%,用时 10. 6 s。 所提出的方法可以实现煤和矸石的精
准识别,这对煤和矸石的智能分选具有重要的研究意义。 |
| 关键词: 多光谱成像技术 黏菌优化 极限学习机分类 波段选择 LBP 算法 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Multi-spectral Identification of Coal and Gangue Based on Slime Mold Algorithm Extreme Learning Machine |
|
ZHOU Mengran1， LING Sheng2， LAI Wenhao1，BIAN Kai1，ZHU Ziwei1，SHEN Ruhan2
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1. School of Electrical and Information Engineering Anhui University of Science & Technology Anhui Huainan 232001
China
2. School of Mechanics and Optoelectronic Physics Anhui University of Science & Technology Anhui Huainan 232001
China
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| Abstract: |
| Accurate identification of coal and gangue is an important prerequisite for coal and gangue sorting and clean
and efficient utilization of coal. In view of the many shortcomings of traditional methods such as low efficiency the need to
install radiation isolation and environmental interference a classification model called Slime Mold Algorithm Extreme
Learning Machine SMA-ELM was proposed to recognize coal and gangue based on multi-spectral image characteristics
and spectral characteristics. A multi-spectral data acquisition system was built to complete the acquisition of spectral
images of coal and gangue. The extracted feature vectors were downscaled by LBP and PCA principal component analysis, and input to SMA-ELM classification model Antlion Algorithm Optimized Extreme Learning Machine ALO-ELM
classification model and Whale Algorithm Optimized Extreme Learning Machine WOA-ELM classification model for
comparison. It focused on the recognition accuracy of coal and gangue under different wavelength responses to screen the
best wavelength and the optimized optimal wavelengths were compared by multiple evaluation indexes. The experimental
results showed that SMA-ELM had the best classification effect and the 6th band was the optimal band the average
recognition accuracy of SMA-ELM in this band was 95. 08% and the recognition F1-Scores of coal and gangue were
96. 47% and 92. 68% respectively with a time of 10. 6 s. The proposed method can achieve the accurate recognition of
coal and gangue which has important research significance for the intelligent separation of coal and gangue. |
| Key words: multi-spectral imaging technology slime molds optimization extreme learning machine classification bands selection LBP algorithm |
