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- W2980963606 abstract "Gases concentration estimation based on the integration of fuzzy partition and model ensemble with drift compensation for metal oxide gas sensors array is proposed in this paper. The sensors response data is divided into several sub-data sets newly according to the drift variation of sensors baseline over time using fuzzy clustering method, and then regression models of these sub-data sets are established based on weighted multi-output support vector regression. The optimal weights of sub-regression models are obtained by traversing search during training process. Based on least squares support vector regression, the optimal weight functions in model ensemble are fitted by training the clustering center and the optimal weights of sub-regression models. In the testing stage, the weights of ensemble models are calculated from the optimal weight function and clustering center. This method can adaptively change the recognition models so that it can cope with the change of sensors drift and ensure the long-term accuracy of concentration quantification." @default.
- W2980963606 created "2019-10-25" @default.
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- W2980963606 date "2019-07-01" @default.
- W2980963606 modified "2023-09-28" @default.
- W2980963606 title "Quantification of Multiple Gases Mixture Based on Fuzzy Partition and Model Ensemble" @default.
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- W2980963606 doi "https://doi.org/10.23919/chicc.2019.8866258" @default.
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