A Model of Classification for E-Nose Based on Genetic Algorithm

被引:5
|
作者
Jiang Min-jun [1 ]
Liu Yunxiang [1 ]
Yang Jingxin [1 ]
Yu Wanjun [1 ]
机构
[1] Shanghai Inst Technol, Comp Sci & Informat Engn Inst, Shanghai 201418, Peoples R China
关键词
Electronic nose; Classification; Genetic algorithms;
D O I
10.4028/www.scientific.net/AMM.475-476.952
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Electronic nose is an intelligent sensory analyzing instrument which simulates the biological olfaction system. Classification is very important for an electronic nose which is usually seen as the software of E-nose. In this paper, we present a model of classification based on genetic algorithm. Compared with common classification algorithms, genetic algorithm had more powerful flexibility and global searching capability. In this paper classification rules were represented in the form of chromosome by binary codes which are in accordance with the features of sensor data. F-measure was used as fitness evaluation. We also designed efficient crossover, mutation operators.
引用
收藏
页码:952 / 955
页数:4
相关论文
共 50 条
  • [21] Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain
    Srivastava, Shubhangi
    Mishra, Gayatri
    Mishra, Hari Niwas
    FOOD CHEMISTRY, 2019, 283 : 604 - 610
  • [22] QBC-Softmax Algorithm for E-nose Data Processing Based on Different Informativeness Evaluations
    He, Zhiyi
    Lv, Kun
    Yu, Song
    Shi, Debo
    Yan, Jia
    PROCEEDINGS OF 2018 IEEE 3RD ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC 2018), 2018, : 2085 - 2088
  • [23] Signal Processing for Multi-sensor E-nose System: Acquisition and Classification
    Rahman, Md. Mizanur
    Charoenlarpnopparut, Chalie
    Suksompong, Prapun
    2015 10TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATIONS AND SIGNAL PROCESSING (ICICS), 2015,
  • [24] Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice
    Qiu, Shanshan
    Gao, Liping
    Wang, Jun
    JOURNAL OF FOOD ENGINEERING, 2015, 144 : 77 - 85
  • [25] e-NOSE response classification of sewage odors by neural networks and fuzzy clustering
    Önkal-Engin, G
    Demir, I
    Engin, SN
    ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS, 2005, 3611 : 648 - 651
  • [26] Intelligent Classifier for E-Nose Systems
    Pelki, Dechen
    Bajo, Javier
    Omatu, Sigeru
    TRENDS IN PRACTICAL APPLICATIONS OF AGENTS, MULTI-AGENT SYSTEMS AND SUSTAINABILITY: THE PAAMS COLLECTION, 2015, 372 : 239 - 240
  • [27] A Novel Label Disentangling Subspace Learning Based on Domain Adaptation for Drift E-Nose Data Classification
    Wang, Zijian
    Duan, Shukai
    Yan, Jia
    IEEE SENSORS JOURNAL, 2023, 23 (19) : 23812 - 23821
  • [28] An e-Nose Fingerprint-Based OC-OCC Classification Approach for Authenticity Identification of Liquors
    Hou, Hui-Rang
    Chen, Yu-Tong
    Qi, Pei-Feng
    Meng, Qing-Hao
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2025, 74
  • [29] Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network
    Adak, M. Fatih
    Yumusak, Nejat
    SENSORS, 2016, 16 (03):
  • [30] Olive Oil Classification and Fraud Detection Using E-Nose and Ultrasonic System
    Zarezadeh, Mohammad Reza
    Aboonajmi, Mohammad
    Varnamkhasti, Mahdi Ghasemi
    Azarikia, Fatemeh
    FOOD ANALYTICAL METHODS, 2021, 14 (10) : 2199 - 2210