The novelty of this work is the correction of measurement errors in a galvanic cell sensor, which might occur due to temperature and humidity variations, using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The training process for the ANN and ANFIS employed 540 data sets consisting of voltage responses of the galvanic cell to known oxygen concentrations, temperatures, and relative humidities. The trained ANFIS was embedded within a central processing unit (CPU) and tested on another 144 data sets with various oxygen concentrations, temperatures, and relative humidities. The deviations for the sensor with ANFIS were less than 0.5 % for 109 data points (75 %), and there was no deviation greater than 2.5 %.

中文翻译：

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应用人工神经网络和自适应神经模糊推理系统来减弱温度和湿度对传感器精度的影响

这项工作的新颖之处在于使用人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）来校正原电池传感器中的测量误差，这些误差可能由于温度和湿度变化而发生。ANN 和 AFIS 的训练过程采用了 540 个数据集，其中包括原电池对已知氧气浓度、温度和相对湿度的电压响应。经过训练的 AFIS 嵌入中央处理单元 (CPU) 中，并在另外 144 个具有不同氧气浓度、温度和相对湿度的数据集上进行测试。具有 AFIS 的传感器的 109 个数据点 (75%) 的偏差小于 0.5%，并且没有偏差大于 2.5%。