Quantification of a Gas Mixture Using a Novel Artificial Neural Network Topology

机译：使用新型人工神经网络拓扑对混合气体进行定量

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High-accuracy evaluation of composition of a gas-mixture (H_2S and NO_2 in air) has been achieved by using a newly-conceived analysis method applied to data from conductance measurements of a 9-element gas-sensor array. An ordinary multi-layer perceptron was fed with data preprocessed by an auxiliary auto-associative neural network performing data compression. Dimensional reduction and ad-hoc training allowed unprecedented accuracy even on a new, non-training data set. The method is largely independent of application and promises wide diffusion.

机译：通过使用一种新概念的分析方法，对气体混合物（H_2S和NO_2在空气中）的组成进行了高精度评估，该分析方法适用于9元素气体传感器阵列的电导测量数据。普通的多层感知器通过执行数据压缩的辅助自缔合神经网络预处理的数据来馈送数据。降维和临时训练即使在新的非训练数据集上也可以实现前所未有的准确性。该方法在很大程度上与应用无关，并有望广泛推广。

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《2nd Italian Conference on Sensors and Microsystems Artificial and Natural Perception Rome, Italy 3-5 February 1997》|1997年|p.212-216|共5页
会议地点 Rome(IT)
作者
Dario Gnani; Vincenzo Guidi; Matteo Ferroni;
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正文语种 eng
中图分类电工技术;
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Quantification of a Gas Mixture Using a Novel Artificial Neural Network Topology

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