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计量学报  2023, Vol. 44 Issue (2): 290-295    DOI: 10.3969/j.issn.1000-1158.2023.02.20
  电离辐射、标准物质与生物计量 本期目录 | 过刊浏览 | 高级检索 |
自适应人工蜂群优化极限学习机在拉曼光谱血液定量分析中的应用
骈斐斐1,王巧云1,2,王铭萱1,张楚1,单鹏1,2,李志刚1,2
1.东北大学信息科学与工程学院,辽宁沈阳110819
2.东北大学 秦皇岛分校河北省微纳精密光学传感与检测技术重点实验室,河北秦皇岛066004
Application of Adaptive Artificial Bee Colony Optimization Extreme Learning Machine in Quantitative Analysis of Blood by Raman Spectroscopy
PIAN Fei-fei1,WANG Qiao-yun1,2,WANG Ming-xuan1,ZHANG Chu1,SHAN Peng1,2,LI Zhi-gang1,2
1. College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
2. Hebei Province Key Laboratory of Micro-Nano Precision Optical Sensing and Detection Technology, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei 066004, China
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摘要 提出了一种基于自适应差分进化人工蜂群优化极限学习机预测血液各组分浓度的方法。首先应用人工蜂群算法对输入权值和隐含层阈值迭代寻优;其次结合差分进化进一步提高模型精度且避免后期易陷入局部最优等问题;由于差分进化算法交叉率和变异率存在凭经验给定的不确定性,最后引入了自适应调整的思想提出自适应差分进化人工蜂群算法优化极限学习机算法的模型,将其应用于血液成分定量分析中。实验表明,自适应差分进化人工蜂群算法优化的极限学习机模型具有较高的预测精度,模型具有较强的稳健性。
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骈斐斐
王巧云
王铭萱
张楚
单鹏
李志刚
关键词 计量学血液检测拉曼光谱极限学习机人工蜂群算法自适应差分进化    
Abstract:A method based on adaptive differential evolution artificial bee colony optimization extreme learning machine is proposed to predict the concentration of each component of blood. First, the artificial bee colony algorithm is used to iteratively optimize the input weights and hidden layer thresholds; secondly, the differential evolution is combined to further improve the model accuracy and avoid problems such as falling into local optimality in the later stage; due to the fact that the crossover rate and mutation rate of the differential evolution algorithm are based on experience, the idea of adaptive adjustment is introduced, and the model of adaptive differential evolution artificial bee colony algorithm to optimize the extreme learning machine algorithm is proposed and applied to the quantitative analysis of blood components. Experiments show that the extreme learning machine model optimized by the adaptive differential evolution artificial bee colony algorithm has high prediction accuracy, and the model has strong robustness.
Key wordsmetrology    Raman spectroscopy    blood detection    extreme learning machine    artificial bee colony algorithm    self-adaption differential evolution
收稿日期: 2021-01-26      发布日期: 2023-02-21
PACS:  TB99  
  TB973  
基金资助:国家自然科学基金(11404054,61601104); 河北省自然科学基金(F2019501025, F2020501040, F2017501052); 中央高校基本科研业务费专项资金(N172304032,2020GFYD026)
通讯作者: 王巧云(1980-),河北秦皇岛人,东北大学副教授,硕士研究生导师,主要从事新型光纤传感器、光声光谱理论及检测技术等方面的研究。Email:wangqiaoyun@neuq.edu.cn     E-mail: wangqiaoyun@neuq.edu.cn
作者简介: 骈斐斐(1996-),河北邯郸人,东北大学在读硕士生,研究方向为中、近红外及拉曼光谱定性定量分析。Email:543808182@qq.com
引用本文:   
骈斐斐,王巧云,王铭萱,张楚,单鹏,李志刚. 自适应人工蜂群优化极限学习机在拉曼光谱血液定量分析中的应用[J]. 计量学报, 2023, 44(2): 290-295.
PIAN Fei-fei,WANG Qiao-yun,WANG Ming-xuan,ZHANG Chu,SHAN Peng,LI Zhi-gang. Application of Adaptive Artificial Bee Colony Optimization Extreme Learning Machine in Quantitative Analysis of Blood by Raman Spectroscopy. Acta Metrologica Sinica, 2023, 44(2): 290-295.
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