Genetic Algorithm Design of Neural Net Based Electronic Nose

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


The training of a multi-layer perceptron using the well known back-propagation algorithm normally takes place after the neural network architecture and the initial values of various network parameters have been defined. Since the success of the training process, in terms of a fast rate of convergence and good generalisation, can be affected by the choice of the architecture and the initial network parameters, much time is spent in searching for the optimal neural paradigm. In this paper, results are presented on the use of Genetic Algorithms to determine automatically a suitable network architecture and a set of parameters from a restricted region of design space. The data-set comes from the response of the Warwick Electronic Nose to a set of simple and complex odours.


Genetic Algorithm Hide Layer Network Parameter Binary String Electronic Nose 
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.


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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

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