Neural Tree Network Based Electronic Nose

  • Adhanom A. Fekadu
  • Evor L. Hines
  • Julian W. Gardner
Conference paper


The training of a multi-layer perceptron using the well known back-propagation algorithm usually requires a priori knowledge of the network architecture. In this paper, results are presented on two practical classification problems which use neural tree classifiers to determine automatically the optimal number of neurons required. The data-set comes from the response of the Warwick Electronic Nose to a set of simple and complex odours.


Internal Node Electronic Nose Decision Tree Classifier Momentum Coefficient Input Feature Vector 
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. [1]
    Shurmer H V, Gardner J W and Chan H T, ‘The application of discrimination techniques in alcohols and tobacco using tin oxide sensors’, Sensors and Actuators, 18, pp 361–371, 1989.CrossRefGoogle Scholar
  2. [2]
    Gardner J W, Bartlett P N, Dodd G H and Shurmer H V, ‘Chemosensory information processing’, ed. D Schild (Berlin: Springer) Vol H39, pp 131, 1990.Google Scholar
  3. [3]
    Gardner J W, Hines E L and Wilkinson M, ‘The application of artificial neural networks to an electronic olfactory system’, Meas. Sci. Technol. 1, pp 446–451, 1990.CrossRefGoogle Scholar
  4. [4]
    Gardner J W, Hines E L and Tang H C, ‘Detection of vapours and odours from a multisensor array using pattern recognition technique. Part 2: Artificial neural networks’, Sensors and Actuators B, 15, pp 9–15, 1992.CrossRefGoogle Scholar
  5. [5]
    Rumelhart D E and McClelland J L, ‘Parallel distributed processing’, MIT press, chapter 8, 1986.Google Scholar
  6. [6]
    Lipmann R P, ‘An introduction to computing with neural nets’, IEEE ASSP Magazine, pp 4-22, April 1987.Google Scholar
  7. [7]
    Safavian S R and Landgrebe D, ‘A survey of Decision Tree Classifier Methodology’, IEEE Trans. Syst., Man., Cybern., vol 21, pp 660–674, 1991.MathSciNetCrossRefGoogle Scholar
  8. [8]
    Brent R P, ‘Fast Training Algorithms for Multilayer Neural Nets’, IEEE Trans. on Neural Networks, pp 346-354, 1991.Google Scholar
  9. [9]
    Sirat J A and Nadal J-P, ‘Neural trees: a new tool for classification’, Network, 1990.Google Scholar
  10. [10]
    Sankar A and Mammone R J, ‘Speaker Independent Vowel Recognition using Neural Tree Networks’.Google Scholar
  11. [11]
    Chan L W and Fallside F, ‘An adaptive training algorithm for back propagation networks’, Computer Speech and Language, 2: 205–218, 1987.CrossRefGoogle Scholar
  12. [12]
    Mammone R J and Zeevi Y Y, ‘Neural Networks: theory and application’, Academic Press, 1991.Google Scholar
  13. [13]
    Hines E L, Gardner J W, Fung W and Fekadu A A, ‘Improved rate of convergence in a MLP based electronic nose’, 2nd Irish Neural Networks Conf., Belfast, June 1992.Google Scholar
  14. [14]
    Leung C W, ‘Application of neural networks to the classification of coffee data’, MSc IT Dissertation, Department of Engineering, University of Warwick, 1991.Google Scholar

Copyright information

© Springer-Verlag/Wien 1993

Authors and Affiliations

  • Adhanom A. Fekadu
    • 1
  • Evor L. Hines
    • 1
  • Julian W. Gardner
    • 1
  1. 1.Department of EngineeringUniversity of WarwickCoventryUK

Personalised recommendations