Neural network based analysis for chemical sensor arrays
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) the sensors are not highly selective.
- Research Organization:
- Pacific Northwest Lab., Richland, WA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States); Associated Western Universities, Inc., Salt Lake City, UT (United States)
- DOE Contract Number:
- FG06-89ER75522; AC06-76RL01830
- OSTI ID:
- 70720
- Report Number(s):
- PNL-SA--24937; CONF-9504137--2; ON: DE95011490
- Country of Publication:
- United States
- Language:
- English
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