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Detection of vapours and odours from a multisensor array using pattern-recognition techniques Part 2. Artificial neural networks

Authors
Journal
Sensors and Actuators B Chemical
0925-4005
Publisher
Elsevier
Publication Date
Volume
9
Issue
1
Identifiers
DOI: 10.1016/0925-4005(92)80187-3
Disciplines
  • Computer Science

Abstract

Abstract Considerable interest has recently arisen in the use of arrays of gas sensors together with an associated pattern-recognition technique to identfy vapours and odours. The performance of the pattern-recognition technique depends upon the choice of parametric expression used to define the array output. At present, there is no generally agreed choice of this parameter for either individual sensors or arrays of sensors. In this paper, we have initially performed a parametric study on experimental data gathered from the response of an array of twelve tin oxide gas sensors to five alcohols and three beers. Five parametric expressions of sensor response are used to characterize the array output, namely, fractional conductance change, relative conductance, log of conductance change and normalized versions of the last two expressions. Secondly, we have applied the technique of artificial neural networks (ANNs) to our preprocessed data. The Rumelhart back-propagation technique is used to train all networks. We find that nearly all of our ANNs can correctly identify all the alcohols using our array of twelve tin oxide sensors and so we use the total sum of squared network errors to determine their relative performance. It is found that the lowest network error occurs for the response parameter defined as the fractional change in conductance, with a value of 1.3 × 10 −4, which is almost half that for the relative conductance. The normalized procedure is also found to improve network performance and so is worthwhile. The optimal network for our data-set is found to contain a single hidden layer of seven elements with a learning rate of 1.0 and momentum term of 0.7, rather than the values of 0.9 and 0.6 recommended by Rumelhart and McClelland, respectively. For this network, the largest output error is less than 0.1. We find that this network outperforms principal-component and cluster analyses (discussed in Part 1) by identifying similar beer odours and offers considerable benefit in its ability to cope with non-linear and highly correlated data.

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