Classification of Different Objects with Artificial Neural Networks Using Electronic Nose

被引：0
|
作者：
Ozsandikcioglu, Umit [1 ]
Atasoy, Ayten [1 ]
Guney, Selda [2 ]
机构：
[1] Karadeniz Tech Univ, Elekt Elekt Muhendisligi Bolumu, Trabzon, Turkey
[2] Baskent Univ, Elekt Elekt Muhendisligi Bolumu, Ankara, Turkey
来源：
2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2015年
关键词：
Electronic nose; Artificial Neural Networks; Classification;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper; an e-nose with low cost which consisting of 8 different gas sensors was used and with this e-nose 9 different odors ((mint, lemon, egg, rotten egg, angelica root, nail polish, naphthalene, rose water, and acetone) was classified. This 9 different odor are classified with Artificial Neural Networks and by using different activation functions, and then the successes of the classification were compared with each other. The maximum success of the testing data is obtained with 100% accuracy rate by using logsig activation function in hidden layer and tansig activation function in output layer. In conclusion; using the chemical database containing the odor of the different objects, distinct odors were shown to be classified correctly.
引用
收藏
页码：815 / 818
页数：4
相关论文
共 50 条
  • [41] Electronic tongue and electronic nose data fusion in classification with neural networks and fuzzy logic based models
    Sundic, T
    Marco, S
    Samitier, J
    Wide, P
    IMTC/2000: PROCEEDINGS OF THE 17TH IEEE INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE: SMART CONNECTIVITY: INTEGRATING MEASUREMENT AND CONTROL, 2000, : 1474 - 1479
  • [42] Automated galaxy classification using artificial neural networks
    Odewahn, SC
    APPLICATIONS OF DIGITAL IMAGE PROCESSING XX, 1997, 3164 : 110 - 119
  • [43] Classification of Electroencephalogram Signals Using Artificial Neural Networks
    Rodrigues, Pedro Miguel
    Teixeira, Joao Paulo
    2010 3RD INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI 2010), VOLS 1-7, 2010, : 808 - 812
  • [44] AUTOMATIC CLASSIFICATION OF ECG USING ARTIFICIAL NEURAL NETWORKS
    Chinese Journal of Biomedical Engineering, 1996, (03) : 135 - 138
  • [45] Protein loop classification using Artificial Neural Networks
    Vieira, A
    Oliva, B
    ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, PROCEEDINGS, 2005, 3594 : 222 - 225
  • [46] Intelligent Classification of Supernovae Using Artificial Neural Networks
    Brito do Nascimento, Francisca Joamila
    Arantes Filho, Luis Ricardo
    Guimaraes, Nogueira Frutuoso
    INTELIGENCIA ARTIFICIAL-IBEROAMERICAL JOURNAL OF ARTIFICIAL INTELLIGENCE, 2019, 22 (63): : 39 - 60
  • [47] Kannada Dialect Classification using Artificial Neural Networks
    Mothukuri, Siva Krishna P.
    Hegde, Pradyoth
    Chittaragi, Nagaratna B.
    Koolagudi, Shashidhar G.
    2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP), 2020,
  • [48] Classification of brain tumours using artificial neural networks
    Rao, B. V. Subba
    Kondaveti, Raja
    Prasad, R. V. V. S. V.
    Shanmukha, V.
    Sastry, K. B. S.
    Dasaradharam, Bh.
    ACTA IMEKO, 2022, 11 (01):
  • [49] ECG rhythm classification using artificial neural networks
    Oien, GE
    Bertelsen, NA
    Eftestol, T
    Husoy, JH
    1996 IEEE DIGITAL SIGNAL PROCESSING WORKSHOP, PROCEEDINGS, 1996, : 514 - 517
  • [50] Classification of prostatic cancer using artificial neural networks
    Mattfeldtt, T
    Gottfried, HW
    Burger, M
    Kestler, HA
    FRACTALS IN BIOLOGY AND MEDICINE, VOL III, 2002, : 101 - 111
12345