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Journal of Sensors
Volume 2015, Article ID 351940, 10 pages
Research Article

Using Bayesian Inference Framework towards Identifying Gas Species and Concentration from High Temperature Resistive Sensor Array Data

1ABB Corporate Research, Windsor, CT 06095, USA
2Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269-3139, USA
3Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT 06269-3222, USA

Received 31 March 2015; Accepted 4 May 2015

Academic Editor: Banshi D. Gupta

Copyright © 2015 Yixin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

The statistical identification result using Artificial Neural Network: A 3-layer BP neural network was utilized for gas identification, in which 12 sets of data (3 types of reducing gases, 4 concentrations for each type of gas) were considered as training data and 1 set of data (125 ppm CO) as test data for simple validation. The initial weights of different layers were randomly generated. To evaluate the performance in terms of the prediction robustness, the prediction results were obtained by repeatedly running 1,000 times and acquiring the statistical distribution.

  1. Supplementary Material