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作 者:骈斐斐 王巧云[1,2] 王铭萱 张楚 单鹏 李志刚[1,2] PIAN Fei-fei;WANG Qiao-yun;WANG Ming-xuan;ZHANG Chu;SHAN Peng;LI Zhi-gang(College of Information Science and Engineering,Northeastern University,Shenyang,Liaoning 110819,China;Hebei Province Key Laboratory of Micro-Nano Precision Optical Sensing and Detection Technology,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066004,China)
机构地区:[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
出 处:《计量学报》2023年第2期290-295,共6页Acta Metrologica Sinica
基 金: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)。
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。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.
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
分 类 号:TB99[一般工业技术—计量学] TB973[机械工程—测试计量技术及仪器]
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