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Proceedings Paper

Neural networks and PCA for determining region of interest in sensory data preprocessing
Author(s): Joakim T. A. Waldemark; Thaddeus A. Roppel; Denise M. Wilson; Kevin L. Dunman; Mary Lou Padgett; Thomas Lindblad
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Paper Abstract

Principal component analysis (PCA) and artificial neural networks are used to investigate electronic gas sensor responses for various alcohol chemicals. PCA is used to identify and visualize the best features to use for classification as well as for detecting outliers. A regular feed forward back propagation neural network (FBP) was used for the actual classification due to the fact that FBP determines better the non-linear borders of the various region of interest involved in the classification. Furthermore, we consider the tradeoff between classification speed and accuracy.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343057
Show Author Affiliations
Joakim T. A. Waldemark, Royal Institute of Technology (Sweden)
Thaddeus A. Roppel, Auburn Univ. (United States)
Denise M. Wilson, Univ. of Kentucky (United States)
Kevin L. Dunman, Auburn Univ. (United States)
Mary Lou Padgett, Auburn Univ. (United States)
Thomas Lindblad, Royal Institute of Technology (Sweden)

Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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