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The application of genetic algorithms to sensor parameter selection for multisensor array configuration
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DOI：10.1016/S0924-4247(99)00195-8.png)
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摘要
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A genetic algorithm search has been applied to the task of optimal array configuration and it is shown that multisensor array performance, in terms of the successful classification of wines, can be improved through the application of such techniques. This is achieved through the utilisation of an objective function, which accounts for sensor sensitivity and repeatability, in order to identify near-optimal subsets of the available parameters. When combined, these subsets of parameters provide discriminable and repeatable fingerprints of odorous materials in classification space which facilitate successful identification of wines to the same degree as much larger arrays. In particular, utilising a thermally cycled 7-element array, which generated 56 parameters, a subset of only 28 parameters was identified which gave an improvement above the classification rate derived from the 56-parameter set. Furthermore, several other optimised parameter sets showed similar results. The role of the objective function is also investigated and shows that further refinement is needed to improve the correlation with the final correct classification rate achieved by the array configuration. These results, along with previous studies, show that through the employment of optimisation techniques, it is often possible to reduce array dimensionality by around 50-70% while still achieving equal, or better, levels of system performance. (C) 1999 Elsevier Science S.A. All rights reserved.
Keyword:
genetic algorithms
sensor parameter selection
multisensor array configuration
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1.5W
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3.3W
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