ICANN ’94 pp 354-357 | Cite as

Sensor arrays and Self-Organizing Maps for Odour Analysis in Artificial Olfactory Systems

  • Fabrizio Davide
  • Corrado Di Natale
  • Arnaldo D’Amico


Our studies have regarded bio-inspired adaptive artificial olfactory systems composed of a sensor array for gas sensing and an artificial neural network, Self-Organizing Topology Preserving Map (SOM), introduced by T. Kohonen (Kohonen, 1989). In order to state the main working principles, Fig. I shows an overview of the digital version of a system for odour classification. The information flows from the left-hand side to the right-hand side: the gas mixtures in the environment determine the m sensor outputs which are sampled and converted into the digital stream z at each clock time. The module implementing the SOM network accepts a sequence of samples by a delay line, classifies the pattern according to its internal class models, and provides a class label as output (Davide et al.,1992 I,II,III).


Sensor Array Sensor Output Single Instruction Multiple Data Odour Recognition Odour Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Davide, F., Di Natale, C., and D’Amico, A., Pattern Recognition Techniques in Gas Sensing, invited at 1st Int. Workshop on New Develop. in Gas Sensors, Castro Marina, Italy, Sept. 13–14, 1993.Google Scholar
  2. Davide, F., Di Natale, C., and D’Amico, A., Sensor Arrays and Neural Networks in Multicomponent Gas Analysis, to be published on Sensors and Actuators B.Google Scholar
  3. Davide, F., Di Natale, C., and D’Amico, A., Self-Organizing Sensory Maps for Odour Classification Mimicking, invited at 2nd CEC Worksh. on Bioelect., Frankfurt, Germany, Nov. 23–25,1993.Google Scholar
  4. Forina, M., Armanino, C., Leardi, R., and Drava, G., Journal of Chemometrics, 5 (1991) 435–453.CrossRefGoogle Scholar
  5. Gardner, J.W., Sensors and Actuators B, 4 (1992) 109–116.CrossRefGoogle Scholar
  6. Kasslin, M., Kangas, J., and Simula, O., Proc. ICANN 92: Artificial Neural Networks 2, Amsterdam. North Holland (1992) 1531–1534.Google Scholar
  7. Kohonen, T., Self-organization and Associative Memory, 3rd eds., Springer-Verlag, Berlin, 1989.Google Scholar
  8. Moore, S.W., et a1., Sensors and Actuators B, 3 (1992) 37–38.Google Scholar
  9. Ritter, H., Martinetz, T., and Schulten, K., Neural Computation and Self-Organizing Maps, Reading, Addison Wesley Publishing Company Inc., 1992.Google Scholar
  10. Vahinger, S. and Göpel, W., in Chemical and Biochemical sensors part I, eds. W. Göpel, T.A. Jones, M. Kleitz, J. Lundström and T. Seiyama, VCH (1991) 191–237.Google Scholar

Copyright information

© Springer-Verlag London Limited 1994

Authors and Affiliations

  • Fabrizio Davide
    • 1
  • Corrado Di Natale
    • 2
  • Arnaldo D’Amico
    • 2
  1. 1.Dipartimento di Ingegneria ElettricaUniversità di L’AquilaL’AquilaItaly
  2. 2.Dipartimento di Ingegneria ElettronicaUniversità di Roma “Tor Vergata”RomaItaly

Personalised recommendations