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Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models

机译：基于改进的LSTM和SVM多类集成学习模型的传感器漂移补偿

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摘要

Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high. We propose a supervised learning algorithm that is based on multi-classifier integration for drift compensation in this paper, which incorporates drift compensation into the classification process, motivated by the fact that the goal of drift compensation is to improve the classification performance. In our method, with the obtained characteristics of sensors and the advantage of Support Vector Machine (SVM) in few-shot classification, the improved Long Shot Term Memory (LSTM) is integrated to build the multi-class classifier model. We tested the proposed approach on the publicly available time series dataset that was collected over three years by the metal-oxide gas sensors. The results clearly indicate the superiority of multiple classifier approach, which achieves higher classification accuracy as compared with different approaches during testing period with an ensemble of classifiers in the presence of sensor drift over time.
机译：漂移是影响传感器可靠性的重要问题，尤其是在气体传感器中。常规方法通常采用参考气体来补偿漂移。但是，其分类精度不高。本文提出了一种基于多分类器集成的漂移补偿监督学习算法，该算法将漂移补偿纳入分类过程中，其目的是提高漂移补偿的目的。在我们的方法中，结合获得的传感器特性和支持向量机（SVM）在几次镜头分类中的优势，集成了改进的长期射击记忆（LSTM）以建立多类别分类器模型。我们在公开的时间序列数据集上测试了该方法，该数据集是由金属氧化物气体传感器在三年内收集的。结果清楚地表明了多重分类器方法的优越性，与测试期间在传感器随时间推移而出现的一组分类器合为一体的情况下相比，不同分类方法具有更高的分类精度。

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