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首页> 外文期刊>Sensors and Actuators. B, Chemical >A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems
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A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems

机译：基于神经网络和自适应神经模糊推理系统的二元混合气定量分类研究

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In this study, the feed forward neural networks (FFNNs) were applied and an adaptive neuro-fuzzy inference system (ANFIS) was proposed for quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures. The quartz crystal microbalance (QCM) type sensors were used as gas sensors. The components in the binary mixture were quantified by applying the steady state sensor responses from the QCM sensor array as inputs to the FFNNs and ANFISs. The back propagation (BP) with momentum and adaptive learning rate algorithm, resilient BP (RP) algorithm, Fletcher-Reeves conjugate-gradient (CG) algorithm, Broyden, Fletcher, Goldfarb, and Shanno quasi-Newton (QN) algorithm, and Levenberg-Marquardt (LM) algorithm were performed as the training methods of the FFNNs. A hybrid training method, which was the combination of least-squares and back propagation algorithms, was used as the training method of the ANFISs. Quantitative analysis of trichloroethylene and acetone was evaluated in terms of training algorithms and methods.
机译：在这项研究中，应用了前馈神经网络（FFNN），并提出了一种自适应神经模糊推理系统（ANFIS）来定量鉴定混合气体中的各个气体浓度（三氯乙烯和丙酮）。石英微天平（QCM）型传感器用作气体传感器。通过应用来自QCM传感器阵列的稳态传感器响应作为FFNN和ANFIS的输入，对二元混合物中的成分进行了定量。具有动量和自适应学习率算法的反向传播（BP）算法，弹性BP（RP）算法，Fletcher-Reeves共轭梯度（CG）算法，Broyden，Fletcher，Goldfarb和Shanno拟牛顿（QN）算法以及Levenberg -Marquardt（LM）算法被用作FFNN的训练方法。最小二乘和反向传播算法相结合的混合训练方法被用作ANFIS的训练方法。根据训练算法和方法对三氯乙烯和丙酮进行了定量分析。

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