Abstract
Prediction of current-voltage characteristics of chemical PbPc sensors with different inter-electrode separation using Neural Networks is carried out successfully. The main purpose of the work is to determine in advance device properties such as current and voltage based on available test data, without the need to build the device. Thus, modification of the design can be carried out based on the optimized and predicted values produced by the Neural Networks model. The produced devices have capabilities to detect small amounts of NO2, which is considered a hazardous gas emitted by various vehicles and can cause undesirable pollution. The used Weight Elimination Algorithm (WEA), proved that as the inter-electrode separation increases, the injected current as a function of applied voltage will also increase, due to more available surface area of vacuum sublimed PbPc material. Also, the response showed non-linearity at larger inter-electrode separations due to separation values and increased bulk interaction effect in addition to the surface interaction of charge transfer. The main benefit of Neural Networks model is to predict values resulting from complex mechanisms, which, otherwise hard to evaluate and model.
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Iskandarani, M.Z. (2020). Application of Neural Networks to Characterization of Chemical Sensors. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1230. Springer, Cham. https://doi.org/10.1007/978-3-030-52243-8_5
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DOI: https://doi.org/10.1007/978-3-030-52243-8_5
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