An Electronic Nose Using Neural Networks with Effective Training Data Selection
Bancha Charumporn, Michifumi Yoshioka, Toru Fujinaka, and Sigeru Omatu
Division of Computer and Systems Science, Graduate School of Engineering Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531 Japan
Received:September 5, 2002Accepted:April 10, 2003Published:August 20, 2003
Keywords:electronic nose, error back-propagation, smoke, training data selection, neural network
An electronic nose developed from metal oxide gas sensors is applied to test smoke of three general household burning materials under different environments. Generally training data is randomly selected for a layered neural network with error back-propagation (BP). Randomized training data always contain redundant data that lengthen training time without improving classification performance. This paper proposes an effective method to select training data based on a similarity index (SI). The SI ensures that only the most valuable training data is included in the training data set. The proposed method is applied to remove redundant data from the training data set before being fed to the layered neural network based on BP. Results verified high classification performance by using a small number of training data from proposed method.
Cite this article as:B. Charumporn, M. Yoshioka, T. Fujinaka, and S. Omatu, “An Electronic Nose Using Neural Networks with Effective Training Data Selection,” J. Robot. Mechatron., Vol.15 No.4, pp. 369-376, 2003.Data files:
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