摘要
拉曼光谱技术由于其快速、简单且无损等优势,广泛地应用于组分的定量分析。目前常用的定量回归方法包括偏最小二乘、人工神经网络、支持向量机等,为寻求新方法,本文对41组葡萄糖样本的拉曼光谱数据研究,以极限学习机为定量回归基础,结合遗传算法、粒子群算法、人工蜂群算法等优化算法,比较分析后提出一种新型自适应差分进化的人工蜂群算法应用于极限学习机,该模型对差分进化的变异率和交叉率做了调整,能够降低极限学习机容易陷入局部最优和差分进化对参数依赖性大的问题,优化后模型的评价指标较传统极限学习机和基于其它优化算法都有显著提升。实验表明,基于自适应差分进化人工蜂群算法的极限学习机提高了预测精确度和模型稳健性。
Raman spectroscopy is widely used in the quantitative analysis of components because of its advantages of fast,simple and nondestructive.Currently,quantitative analysis methods of Raman spectroscopy include Partial Least Squares,Artificial Neural Network,Support Vector Machine,etc.In order to seek new methods,in this paper,the Raman spectroscopy data of 41 groups glucose samples were studied.The Extreme Learning Machine was used for quantitative regression.The optimization algorithms such as Genetic Algorithm,Particle Swarm Optimization Algorithm and Artificial Bee Colony Algorithm were used to improve it.After comparison and analysis,a new type of model was proposed,which called Self Adaption Differential Evolution Artificial Bee Colony Algorithm applied to the Extreme Learning Machine.The model adjusted the mutation rate and crossover rate of differential evolution,which can reduce the influence of the Extreme Learning Machine on local optimization and the differential evolution on parameter dependence.Comparing with the traditional Extreme Learning Machine and other optimization algorithm models,the optimized model evaluation index had a significant boost.Experiment showed that Extreme Learning Machine based on Self Adaption Differential Evolution Artificial Bee Colony Algorithm improved the prediction accuracy and model robustness.
作者
邢凌宇
王巧云
杨磊
尹翔宇
XING Lingyu;WANG Qiaoyun;YANG Lei;YIN Xiangyu(College of information science and engineering, Northeastern University, Shenyang 110819, China)
出处
《光散射学报》
2020年第2期159-165,共7页
The Journal of Light Scattering
基金
河北省自然科学基金项目(F2019501025,F2017501052)
国家自然科学基金项目(61601104)
中央高校基本科研业务费(N172304032)。
关键词
人工蜂群算法
自适应差分进化
拉曼光谱
葡萄糖样本
极限学习机
Artificial Bee Colony Algorithm
Self Adaption Differential Evolution
Raman spectroscopy
glucose sample
Extreme Learning Machine
