基于随机森林的气味感知分类研究  

Research on classification of odor perception based on random forest

作　　者：蒋丹凤 温腾腾 吴黎明[1,2] 王立 Jiang Danfeng;Wen Tengteng;Wu Liming;Wang Li(School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China;Foshan Cangke Intelligent Technology Co.,Ltd.,Foshan 528228,China)

机构地区：[1]广东工业大学机电工程学院,广州510006 [2]佛山沧科智能科技有限公司,佛山528228

出　　处：《电子测量技术》2022年第9期99-103,共5页Electronic Measurement Technology

基　　金：广东省科技计划项目(2019B101001017);佛山广工大研究院创新创业人才团队计划项目(20191108)资助。

摘　　要：机器嗅觉是一种基于传感器阵列与计算机算法模拟生物嗅觉的新兴仿生技术,气味物质气味表征是机器嗅觉值得研究的领域,目前嗅觉感知处于初级研究阶段,气味的通用分类理论基础还不成熟。本文从物质气味电子信息角度出发,利用采集样本中相对均衡香型数据,通过机器学习算法及参数调整、网格搜索等模型优化手段,提出基于电子鼻数据的物质气味分类模型,建立物质气味电子鼻信息与感知联系,实验结果表明,基于随机森林的气味分类在各评价指标上表现突出,平均准确率达到93.6%,随机森林模型相比其他机器学习算法表现优异。Machine olfaction is an emerging bionic technology based on sensor arrays and computer algorithms to simulate biological olfaction. The characterization of odor substances is a field worthy of research in machine olfaction. At present, olfactory perception is in the preliminary research stage, and the general classification theory of odor is not yet mature. In this paper, starting from the electronic information of material odor, aiming at the relatively balanced fragrance data in the collected data, using machine learning algorithms and parameter adjustments, grid search and other model optimization methods, the material odor classification model based on electric nose data is proposed, and the connection between the information and perception of the material odor electronic nose is established. The experimental results show that the random forest model performs better than other machine learning algorithms in each evaluation index, and the average accuracy of odor classification based on random forest reaches 93.6%.

关 键 词：气味分类 机器嗅觉 电子鼻 随机森林 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]