Neural networks and PCA for determining region of interest in sensory data preprocessing

Joakim T. A. Waldemark; Thaddeus A. Roppel; Denise M. Wilson; Kevin L. Dunman; Mary Lou Padgett; Thomas Lindblad

doi:10.1117/12.343057

22 March 1999 Neural networks and PCA for determining region of interest in sensory data preprocessing

Joakim T. A. Waldemark, Thaddeus A. Roppel, Denise M. Wilson, Kevin L. Dunman, Mary Lou Padgett, Thomas Lindblad

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343057
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

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.

Citation Download Citation

Joakim T. A. Waldemark, Thaddeus A. Roppel, Denise M. Wilson, Kevin L. Dunman, Mary Lou Padgett, and Thomas Lindblad "Neural networks and PCA for determining region of interest in sensory data preprocessing", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343057

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