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Proceedings Paper

Neural network based data analysis for chemical sensor arrays
Author(s): Sherif Hashem; Paul E. Keller; Richard T. Kouzes; Lars J. Kangas
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Paper Abstract

Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. In this paper, we examine the effectiveness of using artificial neural networks for real-time data analysis of a sensor array. Analyzing the sensor data in parallel may allow for rapid identification of contaminants in the field without requiring highly selective individual sensors. We use a prototype sensor array which consists of nine tin-oxide Taguchi-type sensors, a temperature sensor, and a humidity sensor. We illustrate that by using neural network based analysis of the sensor data, the selectivity of the sensor array may be significantly improved, especially when some (or all) of the sensors are not highly selective.

Paper Details

Date Published: 6 April 1995
PDF: 8 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205155
Show Author Affiliations
Sherif Hashem, Battelle Memorial Institute/Pacific Northwest Lab. (United States)
Paul E. Keller, Battelle Memorial Institute/Pacific Northwest Lab. (United States)
Richard T. Kouzes, Battelle Memorial Institute/Pacific Northwest Lab. (United States)
Lars J. Kangas, Battelle Memorial Institute/Pacific Northwest Lab. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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