Neural network based data analysis for chemical sensor arrays

Sherif Hashem; Paul E. Keller; Richard T. Kouzes; Lars J. Kangas

doi:10.1117/12.205155

6 April 1995 Neural-network-based data analysis for chemical sensor arrays

Sherif Hashem, Paul E. Keller, Richard T. Kouzes, Lars J. Kangas

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205155
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Sherif Hashem, Paul E. Keller, Richard T. Kouzes, and Lars J. Kangas "Neural-network-based data analysis for chemical sensor arrays", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205155

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