A COMPOSED NEURAL-NETWORK FOR THE RECOGNITION OF GAS-MIXTURES

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.