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A composed neural network for the recognition of gas mixtures

Authors
Journal
Sensors and Actuators B Chemical
0925-4005
Publisher
Elsevier
Publication Date
Volume
25
Identifiers
DOI: 10.1016/0925-4005(95)85180-1
Disciplines
  • Chemistry
  • Computer Science

Abstract

Abstract Artificial neural networks are generally considered as the most promising tools for untangling pattern-recognition problems in chemical sensing. Different neural networks have been shown to be suitable for solving partial aspects of the pattern recognition. For instance, feed-forward networks are particularly able to find out the rules for the feature extraction, while self-organizing maps show better behaviour in classification and identification tasks. In this paper a hybrid network, which exploits the benefits of both these networks, is introduced and applied to the identification of binary mixtures of organic solvent gases using a quartz-microbalance-based sensor array.

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