Journal
SENSORS AND ACTUATORS B-CHEMICAL
Volume 141, Issue 2, Pages 458-464Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2009.06.046
Keywords
VOCs; Classification; Fuzzy ARTMAP; PCA; Sensor array; Pareto optimization
Funding
- Center for Advanced Microelectronics Manufacturing
- State University of New York at Binghamton
- ARL Cooperative [W911NF-07-2-0084]
- U.S. Department of Energy NNSA program
- National Science Foundation
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A Fuzzy ARTMAP classifier for pattern recognition in chemical sensor array was developed based on Fuzzy Set Theory and Adaptive Resonance Theory. In contrast to most current classifiers with difficulty in detecting new analytes, the Fuzzy ARTMAP system can identify untrained analytes with comparatively high probability. And to detect presence of new analyte, the Fuzzy ARTMAP classifier does not need retraining process that is necessary for most traditional neural network classifiers. In this study, principal component analysis (PCA) was first implemented for feature extraction purpose, followed by pattern recognition using Fuzzy ARTMAP classifiers. To construct the classifier with high recognition rate, parameter sensitive analysis was applied to find critical factors and Pareto optimization was used to locate the optimum parameter setting for the classifier. The test result shows that the proposed method can not only maintain satisfactory correct classification rate for trained analytes, but also be able to detect untrained analytes at a high recognition rate. Also the Pareto optimal values of the most important parameter have been identified, which could help constructing Fuzzy ARTMAP classifiers with good classification performance in future application. (C) 2009 Elsevier B.V. All rights reserved.
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