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首页> 外文会议>International Conference onensors, Measurement and Intelligent Materials >Discrimination of Two-Mixture Fragrances Odor Using Artificial Odor Recognition System with Ensemble Backpropagation Neural Networks
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Discrimination of Two-Mixture Fragrances Odor Using Artificial Odor Recognition System with Ensemble Backpropagation Neural Networks

机译：使用具有合并背交神经网络的人工气味识别系统的双混合物香气辨别

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The human sensory test is often used to obtain the sensory quantities of odors, however, the fluctuation of results due to the experts condition can cause discrepancies among panelists. We have developed an artificial odor recognition system using a quartz resonator sensor and backpropagation neural networks as the pattern recognition system in order to eliminate the disadvantage of human panelist system. The backpropagation neural networks shows high recognition rate for single component odor, however, become very low when it is used to discriminate mixture fragrances odor. In this paper we have proposed an ensemble of backpropagation neural networks as the pattern recognition system, and by using the ensemble learning mechanisms, the recognition rate is significantly increased, especially when using ensemble neural networks with five components.
机译：人类感官测试通常用于获得感觉量的气味，然而，由于专家条件导致的结果的波动会导致小组成员之间的差异。我们开发了一种使用石英谐振器传感器和背部衰减神经网络作为模式识别系统的人工气味识别系统，以消除人类专家组系统的缺点。反向衰减神经网络显示出单个组分气味的高识别率，然而，当它用于区分混合物香味时变得非常低。在本文中，我们已经提出了一种反向化神经网络的集合作为模式识别系统，并且通过使用集合学习机制，识别率显着增加，特别是当使用具有五个组件的集合神经网络时。

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